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Periklis Gogas

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Periklis Gogas, 2013. "Business Cycle Synchronization in the European Union: The Effect of the Common Currency," Working Paper series 18_13, Rimini Centre for Economic Analysis.

    Mentioned in:

    1. Euro zone: the common cycle is strong
      by Economic Logician in Economic Logic on 2013-06-13 19:07:00

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Serletis, Apostolos & Gogas, Periklis, 1997. "Chaos in East European black market exchange rates," Research in Economics, Elsevier, vol. 51(4), pages 359-385, December.

    Mentioned in:

    1. Теорија хаоса in Wikipedia (Serbian)
    2. Teori kekacauan in Wikipedia (Malay)
    3. Θεωρία του χάους in Wikipedia (Greek)

Working papers

  1. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    2. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    3. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    4. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.

  2. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.

    Cited by:

    1. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    2. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.

  3. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2017. "Forecasting the U.S. Real House Price Index," Papers 1707.04868, arXiv.org.

    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    2. Vasilios Plakandaras & Rangan Gupta & Constantinos Katrakilidis & Mark E. Wohar, 2017. "Time-Varying Role of Macroeconomic Shocks on House Prices in the US and UK: Evidence from Over 150 Years of Data," Working Papers 201765, University of Pretoria, Department of Economics.
    3. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    4. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    5. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    6. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta, 2018. "Does Inequality Really Matter in Forecasting Real Housing Returns of the United Kingdom?," Working Papers 201859, University of Pretoria, Department of Economics.
    7. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    8. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    9. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    10. Aviral Kumar Tiwari & Rangan Gupta & Mark E. Wohar, 2019. "Is the Housing Market in the United States Really Weakly-Efficient?," Working Papers 201934, University of Pretoria, Department of Economics.
    11. Sommervoll, Åvald & Sommervoll, Dag Einar, 2019. "Learning from man or machine: Spatial fixed effects in urban econometrics," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 239-252.
    12. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    13. McGurk, Zachary, 2020. "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 53-66.
    14. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
    15. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    16. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    17. Narayan, Paresh Kumar & Ahmed, Huson Ali & Narayan, Seema, 2017. "Can investors gain from investing in certain sectors?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 160-177.
    18. Sinha, Ankur & Kedas, Satishwar & Kumar, Rishu & Malo, Pekka, 2019. "Buy, Sell or Hold: Entity-Aware Classification of Business News," IIMA Working Papers WP 2019-04-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of Aging on Urban Land Prices in China," MPRA Paper 89237, University Library of Munich, Germany.

  4. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.

    Cited by:

    1. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Hylton Hollander & Rudi Steinbach, 2020. "Forecasting output growth using a DSGE-based decomposition of the South African yield curve," Empirical Economics, Springer, vol. 58(1), pages 351-378, January.
    3. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).

  5. Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.

    Cited by:

    1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    2. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
    3. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    4. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    5. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    6. Francisco Jareño & Ana Escribano & Zaghum Umar, 2023. "The impact of the COVID-19 outbreak on the connectedness of the BRICS’s term structure," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

  6. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios, 2015. "International Business Cycle Synchronization since the 1870s: Evidence from a Novel Network Approach," MPRA Paper 67223, University Library of Munich, Germany.

    Cited by:

    1. Schmidbauer, Harald & Rösch, Angi & Uluceviz, Erhan, 2017. "Frequency aspects of information transmission in a network of three western equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 933-946.
    2. Sang Hoon Kang & Salim Lahmiri & Gazi Salah Uddin & Jose Arreola Hernandez & Seong-Min Yoon, 2020. "Inflation cycle synchronization in ASEAN countries," Post-Print hal-02779489, HAL.
    3. Amalia Repele & Sébastien Waelti, 2021. "Mapping the Global Business Cycle Network," Open Economies Review, Springer, vol. 32(4), pages 739-760, September.
    4. Tamás Sebestyén & Zita Iloskics, 2020. "Do economic shocks spread randomly?: A topological study of the global contagion network," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    5. Matesanz, David & Ortega, Guillermo J., 2016. "On business cycles synchronization in Europe: A note on network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 287-296.

  7. Periklis Gogas & Rangan Gupta & Stephen M. Miller & Theophilos Papadimitriou & Georgios Antonios Sarantitis, 2015. "Income Inequality: A State-by-State Complex Network Analysis," Working Papers 201534, University of Pretoria, Department of Economics.

    Cited by:

    1. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2019. "Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 65-82, February.
    2. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2017. "Partisan Conflict and Income Distribution in the United States: A Nonparametric Causality-in-Quantiles Approach," Working papers 2017-11, University of Connecticut, Department of Economics.

  8. Sarantitis, Georgios & Papadimitriou, Theophilos & Gogas, Periklis, 2015. "A Network Analysis of the United Kingdom’s Consumer Price Index," DUTH Research Papers in Economics 1-2016, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    2. Emiliano Alvarez & Juan Gabriel Brida & Pablo Mones, 2024. "On the Dynamics of Relative Prices and the Relationship with Inflation: An Empirical Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 339-355, January.
    3. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    4. Qingru Sun & Xiangyun Gao & Ze Wang & Siyao Liu & Sui Guo & Yang Li, 2020. "Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 821-844, October.

  9. Theophilos Papadimitriou & Periklis Gogas & Georgios-Antonios Sarantitis, 2014. "Convergence of European Business Cycles: A Complex Networks Approach," Working Paper series 35_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    2. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    3. Gogas, Periklis & Gupta, Rangan & Miller, Stephen M. & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2017. "Income inequality: A complex network analysis of US states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 423-437.
    4. Mao Takongmo, Charles-O. & Touré, Adam, 2023. "Trade openness and connectedness of national productions: Do financial openness, economic specialization, and the size of the country matter?," Economic Modelling, Elsevier, vol. 125(C).
    5. Amalia Repele & Sébastien Waelti, 2021. "Mapping the Global Business Cycle Network," Open Economies Review, Springer, vol. 32(4), pages 739-760, September.
    6. Plakandaras, Vasilios & Tiwari, Aviral Kumar & Gupta, Rangan & Ji, Qiang, 2020. "Spillover of sentiment in the European Union: Evidence from time- and frequency-domains," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 105-130.
    7. Luis à ngel Hierro & Antonio José Garzón & Helena Domínguez-Torres, 2019. "20 Years of European Monetary Policy. From Doctrinarism to Realpolitik," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 66(3), pages 149-172, December.
    8. Matesanz, David & Ortega, Guillermo J., 2016. "On business cycles synchronization in Europe: A note on network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 287-296.

  10. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    2. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
    3. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    4. Syed Jawad Hussain Shahzad & Clement Kweku Kyei & Rangan Gupta & Eric Olson, 2020. "Investor Sentiment and Dollar-Pound Exchange Rate Returns: Evidence from Over a Century of Data Using a Cross-Quantilogram Approach," Working Papers 202008, University of Pretoria, Department of Economics.
    5. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    6. Omotosho, Babatunde S. & Tumala, Mohammed M., 2019. "A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria," MPRA Paper 98850, University Library of Munich, Germany.
    7. Rahimi, Fatemeh & Mousavian Anaraki, Seyed Alireza, 2020. "Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 423-444, October.
    8. Omotosho, Babatunde S., 2020. "Central Bank Communication during Economic Recessions: Evidence from Nigeria," MPRA Paper 99655, University Library of Munich, Germany.
    9. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.

  11. Theophilos Papadimitriou & Periklis Gogas & Georgios-Antonios Sarantitis, 2014. "European Business Cycle Synchronization: a Complex Network Perspective," Working Paper series 33_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Arnab Chakrabarti & Rituparna Sen, 2018. "Some Statistical Problems with High Dimensional Financial data," Papers 1808.02953, arXiv.org.

  12. Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2014. "US Inflation Dynamics on Long Range Data," Working Papers 201452, University of Pretoria, Department of Economics.

    Cited by:

    1. Yingying XU & Zhixin LIU & Jaime ORTIZ, 2018. "Actual and Expected Inflation in the U.S.: A Time-Frequency View," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 42-62, December.
    2. S. Arshad & S.A.R. Rizvi & O. Haroon & Fahad Mehmood & Q. Gong, 2021. "Are Oil Prices Efficient?," Post-Print hal-04317811, HAL.
    3. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    4. Refk Selmi & Aviral Kumar Tiwari & Shawkat Hammoudeh, 2018. "Efficiency or speculation? A dynamic analysis of the Bitcoin market," Economics Bulletin, AccessEcon, vol. 38(4), pages 2037-2046.
    5. Vasilios Plakandaras & Rangan Gupta & Mark E. Wohar, 2018. "Persistence of Economic Uncertainty: A Comprehensive Analysis," Working Papers 201810, University of Pretoria, Department of Economics.
    6. Yingying Xu & Zhi-Xin Liu & Hsu-Ling Chang & Adelina Dumitrescu Peculea & Chi-Wei Su, 2017. "Does self-fulfilment of the inflation expectation exist?," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1098-1113, March.

  13. Theophilos Papadimitriou & Periklis Gogas & Maria Matthaiou & Efthymia Chrysanthidou, 2014. "Yield curve and Recession Forecasting in a Machine Learning Framework," Working Paper series 32_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
    2. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    3. Yasmeen Idilbi-Bayaa & Mahmoud Qadan, 2021. "Forecasting Commodity Prices Using the Term Structure," JRFM, MDPI, vol. 14(12), pages 1-39, December.
    4. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
    5. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    6. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    7. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.
    8. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    9. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2021. "Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach," Papers 2101.09394, arXiv.org, revised Jan 2022.
    10. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    11. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    12. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    13. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    14. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    15. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    16. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Post-Print hal-03182910, HAL.
    17. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    18. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    19. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
    20. Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.
    21. Andreas Psimopoulos, 2020. "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 18(1), pages 40-99.
    22. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    23. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2023. "Yield spread selection in predicting recession probabilities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1772-1785, November.

  14. Periklis Gogas, 2013. "Business Cycle Synchronization in the European Union: The Effect of the Common Currency," Working Paper series 18_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Alberto Alesina & Guido Tabellini & Francesco Trebbi, 2017. "Is Europe an Optimal Political Area?," CESifo Working Paper Series 6469, CESifo.
    2. Dionysios Chionis & Fotios Mitropoulos & Antonios Sarantidis, 2021. "Business cycles and macroeconomic asymmetries: New evidence from Eurozone and European countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5977-5996, October.
    3. Brunhart, Andreas, 2015. "The Swiss business cycle and the lead of small neighbor Liechtenstein," EconStor Preprints 130154, ZBW - Leibniz Information Centre for Economics.
    4. Ionut Jianu, 2020. "Examining the drivers of business cycle divergence between Euro Area and Romania," Papers 2007.11407, arXiv.org.
    5. Ionuț JIANU, 2020. "Examining the drivers of business cycle divergence between Euro Area and Romania," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(623), S), pages 19-32, Summer.
    6. Ansgar Belke & Clemens Domnick & Daniel Gros, 2017. "Business Cycle Synchronization in the EMU: Core vs. Periphery," Open Economies Review, Springer, vol. 28(5), pages 863-892, November.
    7. Theophilos Papadimitriou & Periklis Gogas & Georgios Sarantitis, 2016. "Convergence of European Business Cycles: A Complex Networks Approach," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 97-119, February.
    8. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    9. Jianu, Ionut, 2020. "Examining the drivers of business cycle divergence between Euro Area and Romania," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 27(2), pages 19-32.
    10. Theophilos Papadimitriou & Periklis Gogas & Georgios-Antonios Sarantitis, 2014. "European Business Cycle Synchronization: a Complex Network Perspective," Working Paper series 33_14, Rimini Centre for Economic Analysis.
    11. Gießler, Stefan & Heinisch, Katja & Holtemöller, Oliver, 2020. "(Since When) Are East and West German Business Cycles Synchronised?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Ahead of , pages 1-28.
    12. Krzysztof Beck, 2016. "Business Cycle Synchronization In European Union: Regional Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 785-815, December.
    13. Andreas Brunhart, 2017. "Are Microstates Necessarily Led by Their Bigger Neighbors’ Business Cycle? The Case of Liechtenstein and Switzerland," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 29-52, May.
    14. Luis à ngel Hierro & Antonio José Garzón & Helena Domínguez-Torres, 2019. "20 Years of European Monetary Policy. From Doctrinarism to Realpolitik," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 66(3), pages 149-172, December.
    15. Theophilos Papadimitriou & Periklis Gogas & Fotios Gkatzoglou, 2022. "The Convergence Evolution in Europe from a Complex Networks Perspective," JRFM, MDPI, vol. 15(10), pages 1-14, October.

  15. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2013. "Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques," Working Paper series 59_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.
    2. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    3. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    4. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.

  16. Ioannis Pragidis & Periklis Gogas & Benjamin Tabak, 2013. "Asymmetric Effects of Monetary Policy in the U.S. and Brazil," Working Papers Series 340, Central Bank of Brazil, Research Department.

    Cited by:

    1. Georgios Georgiadis & Martina Jancokova, 2017. "Financial Globalisation, Monetary Policy Spillovers and Macro-modelling: Tales from 1001 Shocks," Globalization Institute Working Papers 314, Federal Reserve Bank of Dallas.
    2. Bitros, George C., 2021. "Destabilizing asymmetries in central banking: With some enlightenment from money in classical Athens," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    3. Shodipe Oladimeji T. & Shobande Olatunji Abdul, 2021. "Monetary Policy Dynamics in the United States," Open Economics, De Gruyter, vol. 4(1), pages 14-30, January.
    4. Douanla Tayo, Lionel, 2014. "Assessing the effect of monetary policy on economic growth in franc zone," MPRA Paper 60201, University Library of Munich, Germany.
    5. Polyzos, Efstathios, 2022. "Examining the asymmetric impact of macroeconomic policy in the UAE: Evidence from quartile impulse responses and machine learning," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    6. Lau, Wee-Yeap & Yip, Tien-Ming, 2020. "How do monetary transmission channels influence inflation in the short and long run? Evidence from the QQE regime in Japan," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    7. Ajisafe, Rufus A. & Adesina, Kehinde E. & Okunade, Solomon O., 2022. "Effects of Anticipated and Unanticipated Monetary Policy on Output in Nigeria," African Journal of Economic Review, African Journal of Economic Review, vol. 10(2), March.

  17. Ioannis Praggidis & Periklis Gogas & Vasilios Plakandaras & Theophilos Papadimitriou, 2013. "Fiscal shocks and asymmetric effects: a comparative analysis," Papers 1312.2693, arXiv.org.

    Cited by:

    1. Vicente Esteve & Cecilio Tamarit, 2018. "Public debt and economic growth in Spain, 1851–2013," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 12(2), pages 219-249, May.
    2. Clement Olalekan Olaniyi, 2020. "Application of Bootstrap Simulation and Asymmetric Causal Approach to Fiscal Deficit-Inflation Nexus," Global Journal of Emerging Market Economies, Emerging Markets Forum, vol. 12(2), pages 123-140, May.
    3. Ahmad, Ahmad Hassan & Aworinde, Olalekan Bashir & Martin, Christopher, 2015. "Threshold cointegration and the short-run dynamics of twin deficit hypothesis in African countries," The Journal of Economic Asymmetries, Elsevier, vol. 12(2), pages 80-91.

  18. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2013. "Forecasting Bank Credit Ratings," Working Paper series 60_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    2. Zhivaikina, A. & Peresetsky, A., 2017. "Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 49-80.
    3. Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
    4. Valdir Domeneghetti & Fabiano Guasti Lima, 2019. "Strategic direction re-evaluation of bank ratings in Brazil," Economics Bulletin, AccessEcon, vol. 39(2), pages 1336-1347.
    5. Pompella, Maurizio & Dicanio, Antonio, 2017. "Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method," Economic Modelling, Elsevier, vol. 67(C), pages 34-44.
    6. GABAN Lucian & RUS IonuÈ› - Marius & FETITA Alin, 2017. "A Model Of Rating Of Eastern European Banks," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 42-56, August.
    7. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Efterpi Doumpa & Maria Stefanidou, 2020. "Forecasting Credit Ratings of EU Banks," IJFS, MDPI, vol. 8(3), pages 1-15, August.
    9. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    10. John A. Ruddy, 2021. "An Analysis of Bank Financial Strength Ratings and Credit Rating Data," Risks, MDPI, vol. 9(9), pages 1-16, August.
    11. Oliver Takawira & John W. Muteba Mwamba, 2020. "Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 279-299.

  19. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios, 2013. "Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection," DUTH Research Papers in Economics 2-2013, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    2. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    3. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, vol. 8(2), pages 1-22, May.
    4. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    5. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.

  20. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of Simple Sum and Divisia Monetary Aggregates in GDP Forecasting: A Support Vector Machines Approach," Working Paper series 04_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    2. Seitz, Franz & Albuquerque, Bruno & Baumann, Ursel, 2015. "The Information Content Of Money And Credit For US Activity," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113066, Verein für Socialpolitik / German Economic Association.
    3. William A. Barnett & Biyan Tang, 2015. "Chinese Divisia Monetary Index and GDP Nowcasting," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201506, University of Kansas, Department of Economics, revised Nov 2015.
    4. Makram El-Shagi & Kiril Tochkov, 2021. "Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle," CFDS Discussion Paper Series 2021/1, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    5. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    6. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    7. Muhammad Anees Khan & Kumail Abbas & Mazliham Mohd Su’ud & Anas A. Salameh & Muhammad Mansoor Alam & Nida Aman & Mehreen Mehreen & Amin Jan & Nik Alif Amri Bin Nik Hashim & Roslizawati Che Aziz, 2022. "Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach," Sustainability, MDPI, vol. 14(16), pages 1-14, August.

  21. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios, 2013. "Public Debt and Private Consumption in OECD countries," DUTH Research Papers in Economics 1-2013, Democritus University of Thrace, Department of Economics, revised 20 Feb 2014.

    Cited by:

    1. Teboho Jeremiah Mosikari & Joel Hinaunye Eita, 2017. "Empirical test of the Ricardian Equivalence in the Kingdom of Lesotho," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1351674-135, January.
    2. Lorenzo Esposito & Giuseppe Mastromatteo, 2019. "Defaultnomics: Making Sense of the Barro-Ricardo Equivalence in a Financialized World," Economics Working Paper Archive wp_933, Levy Economics Institute.
    3. Teboho Jeremiah Mosikari & Mmamontsho Charlotte Senosi & Joel Hinaunye Eita, 2016. "Manufactured exports and economic growth in Southern African Development Community (SADC) region: A panel cointegration approach," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 12(5), pages 266-278, OCTOBER.
    4. Ian P. Cassar & Kurt Davison & Christian Xuereb, 2018. "Does the Ricardian Equivalence Theorem Capture the Consumption Behavior of Maltese Households?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-77, December.
    5. Mario Coccia, 2018. "National debts and government deficits within European Monetary Union: Statistical evidence of economic issues," Papers 1806.07830, arXiv.org.
    6. Maria Malmierca-Ordoqui & Luis A. Gil-Alana & Lorenzo Bermejo, 2024. "Private and public debt convergence: a fractional cointegration approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(1), pages 161-183, February.
    7. Coccia, Mario, 2017. "Asymmetric paths of public debts and of general government deficits across countries within and outside the European monetary unification and economic policy of debt dissolution," The Journal of Economic Asymmetries, Elsevier, vol. 15(C), pages 17-31.

  22. Theophilos Papadimitriou & Periklis Gogas & Benjamin M. Tabak, 2013. "Complex Networks and Banking Systems Supervision," Working Papers Series 306, Central Bank of Brazil, Research Department.

    Cited by:

    1. Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Andrés De Souza Penaloza & Rodrigo César De Castro Mirand, 2014. "Systemic Risk Measures," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 124, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    2. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria, 2014. "A novel Banking Supervision Method using the Minimum Dominating Set," DUTH Research Papers in Economics 1-2014, Democritus University of Thrace, Department of Economics.
    3. Souza, Sergio Rubens Stancato de & Silva, Thiago Christiano & Tabak, Benjamin Miranda & Guerra, Solange Maria, 2016. "Evaluating systemic risk using bank default probabilities in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 66(C), pages 54-75.
    4. He, Fang & Chen, Xi, 2016. "Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 158-170.
    5. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    6. Thiago C. Silva & Diego R. Amancio & Benjamin M. Tabak, 2020. "Modeling Supply-Chain Networks with Firm-to-Firm Wire Transfers," Papers 2001.06889, arXiv.org, revised Aug 2020.
    7. Theophilos Papadimitriou & Periklis Gogas & Georgios Sarantitis, 2016. "Convergence of European Business Cycles: A Complex Networks Approach," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 97-119, February.
    8. Chabot, Miia & Bertrand, Jean-Louis, 2021. "Complexity, interconnectedness and stability: New perspectives applied to the European banking system," Journal of Business Research, Elsevier, vol. 129(C), pages 784-800.
    9. Kocheturov, A. & Batsyn, M. & Pardalos, P., 2015. "Dynamics of Cluster Structures in Stock Market Networks," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 12-30.
    10. Zappa, Paola & Vu, Duy Q., 2021. "Markets as networks evolving step by step: Relational Event Models for the interbank market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    11. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    12. Silva, Thiago Christiano & de Souza, Sergio Rubens Stancato & Tabak, Benjamin Miranda, 2016. "Network structure analysis of the Brazilian interbank market," Emerging Markets Review, Elsevier, vol. 26(C), pages 130-152.
    13. de Carvalho, Pablo Jose Campos & Gupta, Aparna, 2018. "A network approach to unravel asset price comovement using minimal dependence structure," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 119-132.
    14. Machado, Vicente da Gama & Portugal, Marcelo Savino, 2014. "Measuring inflation persistence in Brazil using a multivariate model," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(2), June.
    15. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    16. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.
    17. Sergio R. S. Souza & Benjamin M. Tabak & Solange M. Guerra, 2013. "Insolvency and Contagion in the Brazilian Interbank Market," Working Papers Series 320, Central Bank of Brazil, Research Department.
    18. Rodrigo César de Castro Miranda & Benjamin Miranda Tabak, 2013. "Contagion Risk within Firm-Bank Bivariate Networks," Working Papers Series 322, Central Bank of Brazil, Research Department.
    19. Apergis, Emmanuel & Apergis, Iraklis & Apergis, Nicholas, 2019. "A new macro stress testing approach for financial realignment in the Eurozone," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 52-80.
    20. Barbi, A.Q. & Prataviera, G.A., 2019. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 876-885.

  23. Papadimitriou, Theophilos & Gogas, Periklis & Plakandaras, Vasilios, 2013. "Forecasting daily and monthly exchange rates with machine learning techniques," DUTH Research Papers in Economics 3-2013, Democritus University of Thrace, Department of Economics, revised 07 Apr 2015.

    Cited by:

    1. Biswas, Rita & Li, Xiao & Piccotti, Louis R., 2023. "Do macroeconomic variables drive exchange rates independently?," Finance Research Letters, Elsevier, vol. 52(C).
    2. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
    3. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    4. Rho Caterina & Fernández Raúl & Palma Brenda, 2021. "A Sentiment-based Risk Indicator for the Mexican Financial Sector," Working Papers 2021-04, Banco de México.
    5. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    7. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
    8. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.
    9. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    10. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    11. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
    12. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "Exchange rate returns and volatility: the role of time-varying rare disaster risks," The European Journal of Finance, Taylor & Francis Journals, vol. 25(2), pages 190-203, January.
    13. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    14. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.
    15. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    16. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.
    17. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    18. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
    19. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    20. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    21. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

  24. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    3. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    4. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.

  25. Serletis, Apostolos & Malliaris, Anastasios & Hinich, Melvin & Gogas, Periklis, 2010. "Episodic Nonlinearity in Leading Global Currencies," DUTH Research Papers in Economics 3-2010, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Rangan Gupta & Anandamayee Majumdar & Mark E. Wohar, 2017. "The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach," Open Economies Review, Springer, vol. 28(1), pages 47-59, February.
    2. Semei Coronado & Omar Rojas & Rafael Romero-Meza & Francisco Venegas-Martinez, 2015. "A study of co-movements between USA and Latin American stock markets: a cross-bicorrelations perspective," Papers 1503.06926, arXiv.org.
    3. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "Some thoughts on accurate characterization of stock market indexes trends in conditions of nonlinear capital flows during electronic trading at stock exchanges in global capital markets," MPRA Paper 49921, University Library of Munich, Germany.
    4. Semei Coronado & Omar Rojas, 2016. "A study of co-movements between oil price, stock index and exchange rate under a cross-bicorrelation perspective: the case of Mexico," Papers 1602.03271, arXiv.org.

  26. Gogas, Periklis & Pragidis, Ioannis, 2010. "Does the Interest Risk Premium Predict Housing Prices?," DUTH Research Papers in Economics 1-2010, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.

  27. Gogas, Periklis & Kothroulas, George, 2009. "Two speed Europe and business cycle synchronization in the European Union: The effect of the common currency," MPRA Paper 13909, University Library of Munich, Germany.

    Cited by:

    1. Degiannakis, Stavros & Duffy, David & Filis, George, 2014. "Business Cycle Synchronisation in EU: A time-varying approach," MPRA Paper 80437, University Library of Munich, Germany.
    2. Degiannakis, Stavros & Duffy, David & Filis, George, 2013. "Time-varying Business Cycles Synchronisation in Europe," MPRA Paper 52925, University Library of Munich, Germany.
    3. Michal Bencik, 2011. "Business cycle synchronisation between the V4 countries and the euro area," Working and Discussion Papers WP 1/2011, Research Department, National Bank of Slovakia.

  28. Gogas, Periklis & Chionis, Dionisios & Pragkidis, Ioannis, 2009. "Predicting European Union recessions in the euro era: The yield curve as a forecasting tool of economic activity," MPRA Paper 13911, University Library of Munich, Germany.

    Cited by:

    1. Theophilos Papadimitriou & Periklis Gogas & Maria Matthaiou & Efthymia Chrysanthidou, 2014. "Yield curve and Recession Forecasting in a Machine Learning Framework," Working Paper series 32_14, Rimini Centre for Economic Analysis.
    2. Petri Kuosmanen & Juuso Vataja, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, John Wiley & Sons, vol. 23(2), pages 90-97, April.
    3. Schock, Matthias, 2014. "Do Eurozone yield spreads predict recessions?," Hannover Economic Papers (HEP) dp-532, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Periklis Gogas & Ioannis Pragidis, 2012. "GDP trend deviations and the yield spread: the case of eight E.U. countries," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(1), pages 226-237, January.
    5. Kuosmanen, Petri & Vataja, Juuso, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, Elsevier, vol. 23(2), pages 90-97.
    6. Goodness C. Aye & Christina Christou & Luis A. Gil-Alana & Rangan Gupta, 2016. "Forecasting the Probability of Recessions in South Africa: The Role of Decomposed Term-Spread and Economic Policy Uncertainty," Working Papers 201680, University of Pretoria, Department of Economics.
    7. Periklis Gogas & Ioannis Pragidis, 2010. "GDP Trend Deviations and the Yield Spread: the Case of Five E.U. Countries," Papers 1005.1326, arXiv.org.
    8. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    9. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.

  29. Serletis, A. & Gogas, P., 1998. "The North American Natural Gas Liquids Markets are Chaotic," Papers 98-10, Calgary - Department of Economics.

    Cited by:

    1. Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
    2. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    3. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    4. Saeed Moshiri & Faezeh Foroutan, 2006. "Forecasting Nonlinear Crude Oil Futures Prices," The Energy Journal, , vol. 27(4), pages 81-96, October.
    5. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2019. "A reappraisal of the chaotic paradigm for energy commodity prices," Energy Economics, Elsevier, vol. 82(C), pages 167-178.
    6. Elder, John & Serletis, Apostolos, 2008. "Long memory in energy futures prices," Review of Financial Economics, Elsevier, vol. 17(2), pages 146-155.
    7. Apostolos Serletis & Ioannis Andreadis, 2007. "Random Fractal Structures in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 18, pages 245-255, World Scientific Publishing Co. Pte. Ltd..
    8. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
    9. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Chaos" in energy and commodity markets: a controversial matter," Papers 1611.07432, arXiv.org, revised Mar 2017.
    10. Wu, Y. & Zhang, D.Z., 2007. "Demand fluctuation and chaotic behaviour by interaction between customers and suppliers," International Journal of Production Economics, Elsevier, vol. 107(1), pages 250-259, May.
    11. William A. Barnett & Hajar Aghababa, 2016. "Dynamic Structure of the Spot Price of Crude Oil: Does Time Aggregation Matter?," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201602, University of Kansas, Department of Economics, revised Aug 2016.
    12. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    13. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.
    14. John Francis Diaz & Jo-Hui Chen, 2017. "Testing for Long-memory and Chaos in the Returns of Currency Exchange-traded Notes (ETNs)," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(4), pages 1-2.
    15. Loretta Mastroeni & Pierluigi Vellucci, 2017. "“Chaos” In Energy And Commodity Markets: A Controversial Matter," Departmental Working Papers of Economics - University 'Roma Tre' 0218, Department of Economics - University Roma Tre.
    16. Tao Yin & Yiming Wang, 2019. "Predicting the Price of WTI Crude Oil Using ANN and Chaos," Sustainability, MDPI, vol. 11(21), pages 1-14, October.
    17. Barkoulas, John T. & Chakraborty, Atreya & Ouandlous, Arav, 2012. "A metric and topological analysis of determinism in the crude oil spot market," Energy Economics, Elsevier, vol. 34(2), pages 584-591.

  30. Serletis, A. & Gogas, P., 1997. "Chaos in East European Black-Market Exchange Rates," Papers 9708, Calgary - Department of Economics.

    Cited by:

    1. William Barnett & Yijun He, 2012. "Unsolved Econometric Problems In Nonlinearity, Chaos, And Bifurcation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201231, University of Kansas, Department of Economics, revised Sep 2012.
    2. Musselwhite, Gary & Herath, Gamini, 2007. "Chaos theory and assessment of forest stakeholder attitudes towards Australian forest policy," Forest Policy and Economics, Elsevier, vol. 9(8), pages 947-964, May.
    3. William Barnett & Apostolos Serletis & Demitre Serletis, 2012. "Nonlinear and Complex Dynamics in Economics," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201238, University of Kansas, Department of Economics, revised Sep 2012.
    4. Bunyamin Demir & Nesrin Alptekin & Yilmaz Kilicaslan & Mehmet Ergen & Nilgun Caglairmak Uslu, 2015. "Forecasting Agricultural Production: A Chaotic Dynamic Approach," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 1(1), pages 65-80, June.
    5. William Barnett & Apostolos Serletis, 2012. "Martingales, Nonlinearity, And Chaos," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201225, University of Kansas, Department of Economics, revised Sep 2012.
    6. Evzen Kocenda & Lubos Briatka, 2004. "Advancing the iid Test Based on Integration across the Correlation Integral: Ranges, Competition, and Power," Econometrics 0409001, University Library of Munich, Germany.
    7. Borusyak, K., 2011. "Nonlinear Dynamics of the Russian Stock Market in Problems of Risk Management," Journal of the New Economic Association, New Economic Association, issue 11, pages 85-105.
    8. Vasilios Plakandaras & Rangan Gupta & Luis A. Gil-Alana & Mark E. Wohar, 2018. "Are BRICS Exchange Rates Chaotic?," Working Papers 201822, University of Pretoria, Department of Economics.
    9. Ritesh Kumar Mishra & Sanjay Sehgal & N.R. Bhanumurthy, 2011. "A search for long‐range dependence and chaotic structure in Indian stock market," Review of Financial Economics, John Wiley & Sons, vol. 20(2), pages 96-104, May.
    10. Musselwhite, Gary & Herath, Gamini, 2004. "A chaos theory interpretation of community perceptions of Australian forest policy," Forest Policy and Economics, Elsevier, vol. 6(6), pages 595-604, October.
    11. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.
    12. William Barnett & Apostolos Serletis & Demitre Serletis, 2005. "Nonlinear and Complex Dynamics in Real Systems," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200517, University of Kansas, Department of Economics, revised Sep 2005.
    13. Goodness C. Aye & Rangan Gupta & Chi Keung Marco Lau & Xin Sheng, 2018. "Is There a Role for Uncertainty in Forecasting Output Growth in OECD Countries? Evidence from a Time Varying Parameter-Panel Vector Autoregressive Model," Working Papers 201823, University of Pretoria, Department of Economics.
    14. Evzen Kocenda, 2003. "An Alternative to the BDS Test: Integration Across The Correlation Integral," Econometrics 0301004, University Library of Munich, Germany.
    15. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    16. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    17. Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2015. "US inflation dynamics on long-range data," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3874-3890, August.
    18. McKenzie, Michael D., 2001. "Chaotic behavior in national stock market indices: New evidence from the close returns test," Global Finance Journal, Elsevier, vol. 12(1), pages 35-53.
    19. Evzen Kocenda & Lubos Briatka, 2005. "Optimal Range for the iid Test Based on Integration Across the Correlation Integral," Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 265-296.
    20. Anagnostidis, Panagiotis & Emmanouilides, Christos J., 2015. "Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 473-487.

  31. Gogas, Periklis, 1997. "On the construction of cersonal, corporate and effective overall marginal tax rates for Canada (1977-1992)," MPRA Paper 1465, University Library of Munich, Germany.

    Cited by:

    1. Sinha, Pankaj & Bansal, Vishakha, 2012. "Algorithm for calculating corporate marginal tax rate using Monte Carlo simulation," MPRA Paper 40811, University Library of Munich, Germany.

  32. Apostolos Serletis & Periklis Gogas, "undated". "Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions," Working Papers 2013-02, Department of Economics, University of Calgary.

    Cited by:

    1. William A. Barnett, 2013. "Friedman and Divisia Monetary Measures," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201312, University of Kansas, Department of Economics, revised Dec 2013.
    2. Michael T. Belongia & Peter N. Ireland, 2019. "A Classical View of the Business Cycle," NBER Working Papers 26056, National Bureau of Economic Research, Inc.
    3. Seitz, Franz & Albuquerque, Bruno & Baumann, Ursel, 2015. "The Information Content Of Money And Credit For US Activity," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113066, Verein für Socialpolitik / German Economic Association.
    4. William A. Barnett & Biyan Tang, 2015. "Chinese Divisia Monetary Index and GDP Nowcasting," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201506, University of Kansas, Department of Economics, revised Nov 2015.
    5. Stokes, Houston H., 2016. "Using nonlinear testing procedures to specify the right hand side of an aggregate production function containing financial variables in the period 1967–2011," The Journal of Economic Asymmetries, Elsevier, vol. 14(PB), pages 147-156.
    6. Apostolos Serletis & Libo Xu, "undated". "Consumption, Leisure, and Money," Working Papers 2019-08, Department of Economics, University of Calgary, revised 06 Jul 2019.
    7. Barnett, William & Su, Liting, 2016. "Risk Adjustment of the Credit-Card Augmented Divisia Monetary Aggregates," Studies in Applied Economics 67, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    8. Barnett, William & Su, Liting, 2017. "Financial Firm Production of Inside Monetary and Credit Card Services: An Aggregation Theoretic Approach," MPRA Paper 82061, University Library of Munich, Germany.
    9. William A. Barnett & Neepa Gaekwad, 2021. "Multilateral Divisia Monetary Aggregates for the Euro Area," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202108, University of Kansas, Department of Economics, revised Jan 2021.
    10. Makram El-Shagi & Kiril Tochkov, 2021. "Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle," CFDS Discussion Paper Series 2021/1, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    11. Per Hjertstrand & James L. Swofford & Gerald A. Whitney, 2016. "Mixed Integer Programming Revealed Preference Tests of Utility Maximization and Weak Separability of Consumption, Leisure, and Money," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(7), pages 1547-1561, October.
    12. Michael T. Belongia & Peter N. Ireland, 2017. "The Demand for Divisia Money: Theory and Evidence," Boston College Working Papers in Economics 937, Boston College Department of Economics.
    13. Barnett, William & Suvra Bhadury, Soumya & Ghosh, Taniya, 2015. "An SVAR Approach to Evaluation of Monetary Policy in India: Solution to the Exchange Rate Puzzles in an Open Economy," Studies in Applied Economics 41, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    14. William A. Barnett & Liting Su, 2016. "Joint aggregation over money and credit card services under risk," Economics Bulletin, AccessEcon, vol. 36(4), pages 2301-2310.
    15. William A. Barnett & Neepa B. Gaekwad, 2018. "The Demand for Money for EMU: a Flexible Functional Form Approach," Open Economies Review, Springer, vol. 29(2), pages 353-371, April.
    16. William A. Barnett & Marcelle Chauvet & Danilo Leiva‐Leon & Liting Su, 2024. "The Credit‐Card‐Services Augmented Divisia Monetary Aggregates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1163-1202, August.
    17. William A. Barnett & Qing Han & Jianbo Zhang, 2018. "Monetary Services Aggregation under Uncertainty: A Behavioral Economics Extension Using Choquet Expectation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201806, University of Kansas, Department of Economics, revised Aug 2018.
    18. Michael T. Belongia & Peter N. Ireland, 2019. "A Reconsideration of Money Growth Rules," Boston College Working Papers in Economics 976, Boston College Department of Economics.
    19. Apostolos Serletis & Khandokar Istiak, "undated". "Are the Responses of the U.S. Economy Asymmetric to Positive and Negative Money Supply Shocks?," Working Papers 2015-17, Department of Economics, University of Calgary, revised 10 Aug 2015.
    20. Apostolos Serletis, "undated". "Monetary Policy and Leverage Shocks," Working Papers 2016-45, Department of Economics, University of Calgary, revised 23 Nov 2016.
    21. William A. Barnett & Kun He & Jingtong He, 2022. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," JRFM, MDPI, vol. 15(10), pages 1-17, October.
    22. Dery, Cosmas & Serletis, Apostolos, 2021. "Interest Rates, Money, And Economic Activity," Macroeconomic Dynamics, Cambridge University Press, vol. 25(7), pages 1842-1891, October.
    23. Barnett, William & Chauvet, Marcelle & Leiva-Leon, Danilo & Su, Liting, 2016. "Nowcasting nominal gdp with the credit-card augmented Divisia monetary aggregates," MPRA Paper 73246, University Library of Munich, Germany.
    24. Istiak, Khandokar & Serletis, Apostolos, 2016. "A Note On Leverage And The Macroeconomy," Macroeconomic Dynamics, Cambridge University Press, vol. 20(1), pages 429-445, January.
    25. Libo Xu & Apostolos Serletis, 2022. "The Demand for Assets: Evidence from the Markov Switching Normalized Quadratic Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(4), pages 989-1025, June.
    26. Karl Pinno & Apostolos Serletis, "undated". "Money, Velocity, and the Stock Market," Working Papers 2016-33, Department of Economics, University of Calgary, revised 06 Jun 2016.
    27. Masudul Hasan Adil & Rafiq Hussain & Adelajda Matuka, 2022. "Interest rate sensitivity of demand for money and effectiveness of monetary policy: fresh evidence from combined cointegration test and ARDL approach," SN Business & Economics, Springer, vol. 2(7), pages 1-24, July.
    28. William A. Barnett & Hyun Park, 2023. "Have Credit Card Services Become Important to Monetary Aggregation? An Application of Sign Restricted Bayesian VAR," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202304, University of Kansas, Department of Economics.
    29. Apostolos Serletis & Sajjadur Rahman, 2015. "On the Output Effects of Monetary Variability," Open Economies Review, Springer, vol. 26(2), pages 225-236, April.
    30. William A. Barnett & Ryadh M. Alkhareif, 2015. "Modern and Traditional Methods for Measuring Money Supply: The Case of Saudi Arabia," IJFS, MDPI, vol. 3(1), pages 1-7, February.
    31. Barnett, William A. & Ghosh, Taniya & Adil, Masudul Hasan, 2022. "Is money demand really unstable? Evidence from Divisia monetary aggregates," MPRA Paper 111762, University Library of Munich, Germany.
    32. Ali Jadidzadeh & Apostolos Serletis, 2019. "The Demand for Assets and Optimal Monetary Aggregation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(4), pages 929-952, June.
    33. Anderson, Richard G. & Duca, John V. & Fleissig, Adrian R. & Jones, Barry E., 2019. "New monetary services (Divisia) indexes for the post-war U.S," Journal of Financial Stability, Elsevier, vol. 42(C), pages 3-17.
    34. Scharnagl, Michael & Mandler, Martin, 2015. "The relationship of simple sum and Divisia monetary aggregates with real GDP and inflation: a wavelet analysis for the US," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112879, Verein für Socialpolitik / German Economic Association.
    35. de la Horra, Luis P. & de la Fuente, Gabriel & Perote, Javier, 2019. "The drivers of Bitcoin demand: A short and long-run analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 21-34.
    36. Liu, Jinan & Dery, Cosmas & Serletis, Apostolos, 2020. "Recent monetary policy and the credit card-augmented Divisia monetary aggregates," Journal of Macroeconomics, Elsevier, vol. 64(C).
    37. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    38. Barnett, William A., 2014. "The joint services of money and credit," MPRA Paper 60336, University Library of Munich, Germany.
    39. Ioannis Andreadis & Athanasios D. Fragkou & Theodoros E. Karakasidis & Apostolos Serletis, 2023. "Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-17, December.
    40. Serletis, Apostolos & Xu, Libo, 2020. "Functional monetary aggregates, monetary policy, and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    41. Zhan, Minghua & Wang, Lijun & Zhan, Shuwei & Lu, Yao, 2023. "Does digital finance change the stability of money demand function? Evidence from China," Journal of Asian Economics, Elsevier, vol. 88(C).
    42. Chen, Zhengyang & Valcarcel, Victor J., 2024. "A granular investigation on the stability of money demand," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue FirstView, pages 1-26.
    43. Cosmas Dery & Apostolos Serletis, 2023. "Macroeconomic Fluctuations in the United States: The Role of Monetary and Fiscal Policy Shocks," Open Economies Review, Springer, vol. 34(5), pages 961-977, November.
    44. William A. Barnett & Liting Su, 2017. "Financial Firm Production Of Inside Monetary And Credit Card Services: An Aggregation Theoretic Approach1," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201707, University of Kansas, Department of Economics, revised Oct 2017.

Articles

  1. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

    Cited by:

    1. Piotr F. Borowski, 2022. "Mitigating Climate Change and the Development of Green Energy versus a Return to Fossil Fuels Due to the Energy Crisis in 2022," Energies, MDPI, vol. 15(24), pages 1-16, December.
    2. Wioletta Czemiel-Grzybowska, 2022. "Conceptualization and Mapping of Predictors of Technological Entrepreneurship Growth in a Changing Economic Environment (COVID-19) from the Polish Energy Sector," Energies, MDPI, vol. 15(18), pages 1-14, September.

  2. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

    Cited by:

    1. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    2. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
    3. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    4. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    5. Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
    6. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
    7. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).

  3. Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2022. "Mind the gap: forecasting euro-area output gaps with machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 29(19), pages 1824-1828, November.

    Cited by:

    1. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.

  4. Anna Agrapetidou & Paulos Charonyktakis & Periklis Gogas & Theophilos Papadimitriou & Ioannis Tsamardinos, 2021. "An AutoML application to forecasting bank failures," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 5-9, January.

    Cited by:

    1. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.

  5. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.

    Cited by:

    1. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    2. Ahmad El Majzoub & Fethi A. Rabhi & Walayat Hussain, 2023. "Evaluating interpretable machine learning predictions for cryptocurrencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 137-149, July.
    3. Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    4. Sergio Mariotti, 2021. "Forging a new alliance between economics and engineering," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(4), pages 551-572, December.
    5. Nwafor, Chioma Ngozi & Nwafor, Obumneme Zimuzor, 2023. "Determinants of non-performing loans: An explainable ensemble and deep neural network approach," Finance Research Letters, Elsevier, vol. 56(C).
    6. Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
    7. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.
    8. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    9. Adil EL Fakir & Richard Fairchild & Youssef Lamrani Alaoui & Dora Chan & Mohamed Tkiouat & Zaid Amer, 2024. "Kinship, gender and social links impact on micro group lending defaults," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2527-2542, April.
    10. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    11. Steve J. Bickley & Benno Torgler, 2021. "Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory," CREMA Working Paper Series 2021-21, Center for Research in Economics, Management and the Arts (CREMA).
    12. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.

  6. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.

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    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
    3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    4. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

  7. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

    Cited by:

    1. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    2. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).

  8. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.

    Cited by:

    1. Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Forecasting with Deep Learning: S&P 500 index," Papers 2103.14080, arXiv.org.

  9. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Efterpi Doumpa & Maria Stefanidou, 2020. "Forecasting Credit Ratings of EU Banks," IJFS, MDPI, vol. 8(3), pages 1-15, August.

    Cited by:

    1. Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.

  10. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.

    Cited by:

    1. Weifan Zhong & Lijing Du, 2023. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    2. Jackson, Ilya & Ivanov, Dmitry, 2023. "A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    3. Cheng, Long & Yang, Junjian & Chen, Xuewu & Cao, Mengqiu & Zhou, Hang & Sun, Yu, 2020. "How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?," Journal of Transport Geography, Elsevier, vol. 89(C).

  11. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.

    Cited by:

    1. Joseph G. Haubrich, 2020. "Does the Yield Curve Predict Output?," Working Papers 20-34, Federal Reserve Bank of Cleveland.
    2. Elie Bouri & Rangan Gupta & Christian Pierdzioch & Onur Polat, 2024. "Forecasting U.S. Recessions Using Over 150 Years of Data: Stock-Market Moments versus Oil-Market Moments," Working Papers 202435, University of Pretoria, Department of Economics.
    3. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).

  12. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.

    Cited by:

    1. Jean-Armand Gnagne & Kevin Moran, 2020. "Forecasting Bank Failures in a Data-Rich Environment," Working Papers 20-13, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    2. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    3. Andrés Giovanni Camacho Ardila & Federico Hernández Álvarez & Luis Ignacio Román de la Sancha, 2023. "Ciclos en el Sector Bancario Mexicano: un Índice Coincidente (CP1G7) vía ACP," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(4), pages 1-25, Octubre -.
    4. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    5. Małgorzata Iwanicz-Drozdowska & Krzysztof Jackowicz & Maciej Karczmarczyk, 2021. "“The Crooked Smile of TCR†: Banks’ Solvency and Restructuring Costs in the European Banking Industry," SAGE Open, , vol. 11(3), pages 21582440211, September.
    6. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    7. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    8. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    9. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    10. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    11. Anton Gerunov, 2023. "Modern Approaches To Forecasting Firm Default Rates Over The Short To Medium Term: An Application To A Panel Of Polish Companies," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 22(1), pages 5-15, October.
    12. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    13. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    14. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    15. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
    16. Caifeng Liu & Wenfeng Pan & Hongcheng Zhou, 2023. "RCML: A Novel Algorithm for Regressing Price Movement during Commodity Futures Stress Testing Based on Machine Learning," JRFM, MDPI, vol. 16(6), pages 1-12, May.
    17. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    18. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    19. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
    20. Periklis Gogas & Theophilos Papadimitriou & Maria-Artemis Matthaiou, 2022. "Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS)," JRFM, MDPI, vol. 15(6), pages 1-13, June.
    21. Jean Armand Gnagne & Kevin Moran, 2018. "Monitoring Bank Failures in a Data-Rich Environment," Cahiers de recherche 1815, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    22. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
    23. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    24. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
    25. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    26. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    27. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).

  13. Gogas, Periklis & Pragidis, Ioannis & Tabak, Benjamin M., 2018. "Asymmetric effects of monetary policy in the U.S and Brazil," The Journal of Economic Asymmetries, Elsevier, vol. 18(C), pages 1-1.
    See citations under working paper version above.
  14. Athanasia Dimitriadou & Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras, 2018. "Oil Market Efficiency under a Machine Learning Perspective," Forecasting, MDPI, vol. 1(1), pages 1-12, October.

    Cited by:

    1. Mangku Purnomo & Fenna Otten & Heiko Faust, 2018. "Indonesian Traditional Market Flexibility Amidst State Promoted Market Competition," Social Sciences, MDPI, vol. 7(11), pages 1-17, November.
    2. Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.

  15. Georgios Antonios Sarantitis & Theophilos Papadimitriou & Periklis Gogas, 2018. "A Network Analysis of the United Kingdom’s Consumer Price Index," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 173-193, February.
    See citations under working paper version above.
  16. Gogas, Periklis & Gupta, Rangan & Miller, Stephen M. & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2017. "Income inequality: A complex network analysis of US states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 423-437.

    Cited by:

    1. Benra, Felipe & Nahuelhual, Laura, 2019. "A trilogy of inequalities: Land ownership, forest cover and ecosystem services distribution," Land Use Policy, Elsevier, vol. 82(C), pages 247-257.

  17. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    See citations under working paper version above.
  18. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    See citations under working paper version above.
  19. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2016. "Testing Exchange Rate Models in a Small Open Economy: an SVR Approach," Bulletin of Applied Economics, Risk Market Journals, vol. 3(2), pages 9-29.

    Cited by:

    1. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    2. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.

  20. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.

    Cited by:

    1. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.

  21. Theophilos Papadimitriou & Periklis Gogas & Georgios Sarantitis, 2016. "Convergence of European Business Cycles: A Complex Networks Approach," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 97-119, February.
    See citations under working paper version above.
  22. Periklis Gogas & Theophilos Papadimitriou & Efthymia Chrysanthidou, 2015. "Yield Curve Point Triplets in Recession Forecasting," International Finance, Wiley Blackwell, vol. 18(2), pages 207-226, June.

    Cited by:

    1. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    2. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    3. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    4. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    5. Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
    6. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).

  23. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    See citations under working paper version above.
  24. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    See citations under working paper version above.
  25. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis & Diamantaras, Konstantinos, 2015. "Market sentiment and exchange rate directional forecasting," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 69-79.
    See citations under working paper version above.
  26. Periklis Gogas & Ioannis Pragidis, 2015. "Are there asymmetries in fiscal policy shocks?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 42(2), pages 303-321, May.

    Cited by:

    1. Bahmani-Oskooee, Mohsen & Ardakani, Amid, 2020. "Does GINI respond to income volatility in an asymmetric manner? Evidence from 41 countries," Economic Systems, Elsevier, vol. 44(2).
    2. BAHMANI-OSKOOEE, Mohsen & Fariditavana, Hadiseh, 2016. "How Sensitive are the U.S. Inpayments and Outpayments to Exchange Rate Changes: An Asymmetry Analysis," MPRA Paper 81829, University Library of Munich, Germany, revised 06 Jan 2017.
    3. Mohsen Bahmani-Oskooee & Hanafiah Harvey, 2018. "Do inpayments and outpayments respond to exchange rate changes asymmetrically: Evidence from Malaysia," The International Trade Journal, Taylor & Francis Journals, vol. 32(4), pages 317-342, August.
    4. Mohsen Bahmani-Oskooee & Sujata Saha, 2021. "On the asymmetric effects of exchange rate volatility on the trade flows of India with each of its fourteen partners," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 14(1), pages 66-85, January.
    5. Mohsen Bahmani-Oskooee & Majid Maki-Nayeri, 2019. "Asymmetric Effects of Policy Uncertainty on Domestic Investment in G7 Countries," Open Economies Review, Springer, vol. 30(4), pages 675-693, September.
    6. Mohsen Bahmani-Oskooee & Majid Maki Nayeri, 2018. "Policy Uncertainty and the Demand for Money in Korea: An Asymmetry Analysis," International Economic Journal, Taylor & Francis Journals, vol. 32(2), pages 219-234, April.
    7. Mohsen Bahmani-Oskooee & Muhammad Aftab, 2019. "Malaysia-Japan Commodity Trade and Asymmetric Effects of Exchange Rate Changes," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 14(2), pages 220-263, June.
    8. Mohsen Bahmani-Oskooee & Augustine C. Arize, 2020. "Asymmetric response of domestic production to exchange rate changes: evidence from Africa," Economic Change and Restructuring, Springer, vol. 53(1), pages 1-24, February.
    9. Jia Xu & Mohsen Bahmani‐Oskooee & Huseyin Karamelikli, 2022. "On the link between U.S.‐China commodity trade and exchange rate uncertainty: An asymmetric analysis," Australian Economic Papers, Wiley Blackwell, vol. 61(1), pages 87-137, March.
    10. Mohsen Bahmani-Oskooee & Ridha Nouira, 2021. "U.S. – Italy commodity trade and the J-curve: new evidence from asymmetry analysis," International Economics and Economic Policy, Springer, vol. 18(1), pages 73-103, February.
    11. Bahmani-Oskooee, Mohsen & Hasanzade, Mehrnoosh & Bahmani, Sahar, 2022. "Stock returns and income inequality: Asymmetric evidence from state level data in the U.S," Global Finance Journal, Elsevier, vol. 52(C).
    12. Mohsen Bahmani-Oskooee & Thouraya Hadj Amor & Hanafiah Harvey & Huseyin Karamelikli, 2019. "Is there a J-curve effect in Tunisia’s bilateral trade with her partners? New evidence from asymmetry analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 1-18, February.
    13. Mohsen Bahmani‐Oskooee & Niloy Bose & Yun Zhang, 2019. "An asymmetric analysis of the J‐curve effect in the commodity trade between China and the US," The World Economy, Wiley Blackwell, vol. 42(10), pages 2854-2899, October.
    14. Mohsen Bahmani‐Oskooee & Jungho Baek, 2020. "On the asymmetric effects of the real exchange rate on domestic investment in G7 countries," Australian Economic Papers, Wiley Blackwell, vol. 59(4), pages 303-318, December.
    15. Mohsen Bahmani-Oskooee & Huseyin Karamelikli, 2021. "The Turkey-US commodity trade and the asymmetric J-curve," Economic Change and Restructuring, Springer, vol. 54(4), pages 943-973, November.
    16. Mohsen Bahmani-Oskooee & Hanafiah Harvey & Amr Hosny, 2019. "Kazakhstan trade with its partners and the role of tenge: an asymmetric analysis," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(4), pages 493-513, December.
    17. Bahmani-Oskooee, Mohsen & Kanitpong, Tatchawan, 2019. "Thailand-China commodity trade and exchange rate uncertainty: Asymmetric evidence from 45 industries," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    18. Syed Ali Raza & Syed Zaki Hassan & Arshian Sharif, 2019. "Asymmetric Relationship Between Government Revenues and Expenditures in a Developing Economy: Evidence from a Non-linear Model," Global Business Review, International Management Institute, vol. 20(5), pages 1179-1195, October.
    19. Bahmani-Oskooee, Mohsen & Maki Nayeri, Majid, 2020. "Policy uncertainty and the demand for money in the United Kingdom: Are the effects asymmetric?," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 76-84.
    20. Bahmani-Oskooee, Mohsen & Aftab, Muhammad, 2017. "Asymmetric effects of exchange rate changes on the Malaysia-China commodity trade," MPRA Paper 83024, University Library of Munich, Germany, revised 16 Mar 2017.
    21. Bahmani-Oskooee, Mohsen & Arize, Augustine C., 2022. "The effect of exchange rate volatility on U.S. bilateral trade with Africa: A symmetric and asymmetric analysis," Economic Systems, Elsevier, vol. 46(1).
    22. Mohsen Bahmani-Oskooee & Hadiseh Fariditavana, 2019. "How sensitive are the U.S. inpayments and outpayments to real exchange rate changes: an asymmetry analysis," International Economics and Economic Policy, Springer, vol. 16(4), pages 619-647, October.
    23. Mohsen Bahmani-Oskooee & Dan Xi & Sahar Bahmani, 2019. "More evidence on the asymmetric effects of exchange rate changes on the demand for money: evidence from Asian," Applied Economics Letters, Taylor & Francis Journals, vol. 26(6), pages 485-495, March.
    24. Mohsen Bahmani‐Oskooee & Hanafiah Harvey, 2022. "The U.S.‐Canadian trade and exchange rate uncertainty: Asymmetric evidence from commodity trade," The World Economy, Wiley Blackwell, vol. 45(3), pages 841-866, March.
    25. Mohsen Bahmani-Oskooee & Huseyin Karamelikli, 2022. "Exchange Rate Volatility and Commodity Trade between U.K. and China: An Asymmetric Analysis," Chinese Economy, Taylor & Francis Journals, vol. 55(1), pages 41-65, January.
    26. Bahmani-Oskooee, Mohsen & Motavallizadeh-Ardakani, Amid, 2017. "On the value of the dollar and income inequality: Asymmetric evidence from state level data in the U.S," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 64-78.
    27. Mohsen Bahmani‐Oskooee & Hanafiah Harvey & Scott W. Hegerty, 2018. "The real peso–dollar rate and US–Mexico industry trade: an asymmetric analysis," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(4), pages 350-389, September.
    28. Mohsen Bahmani‐Oskooee & Abera Gelan, 2020. "The South Africa‐U.S. Trade and the Real Exchange Rate: Asymmetric Evidence from 25 Industries," South African Journal of Economics, Economic Society of South Africa, vol. 88(2), pages 186-203, June.
    29. Mohsen Bahmani-Oskooee & Majid Maki-Nayeri, 2018. "Asymmetric Effects of Policy Uncertainty on the Demand for Money in the United States," JRFM, MDPI, vol. 12(1), pages 1-13, December.
    30. Mohsen Bahmani-Oskooee & Ilir Miteza & Altin Tanku, 2020. "Exchange rate changes and money demand in Albania: a nonlinear ARDL analysis," Economic Change and Restructuring, Springer, vol. 53(4), pages 619-633, November.
    31. Mohsen Bahmani‐Oskooee & Jungho Baek, 2021. "On the asymmetric effects of exchange‐rate volatility on trade flows: Evidence from Korea‐U.S. commodity trade," Australian Economic Papers, Wiley Blackwell, vol. 60(4), pages 594-629, December.
    32. Mohsen Bahmani-Oskooee & Hanafiah Harvey, 2017. "The Asymmetric Effects of Exchange Rate Changes on the Trade Balance of Singapore," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., vol. 17(4), pages 1-11, December.
    33. Mohsen Bahmani-Oskooee & Huseyin Karamelikli & Farhang Niroomand, 2023. "Asymmetric effects of exchange rate volatility on trade flows: evidence from G7," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(1), pages 38-62, March.
    34. VODĂ Alina Daniela & DOBROTĂ Gabriela & CRISTEA Loredana Andreea, 2020. "Heterogeneity Of Fiscal Policies," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 257-264, December.
    35. Bahmani-Oskooee, Mohsen & Karamelikli, Huseyin, 2021. "Financial and insurance services trade and role of the exchange rate: An asymmetric analysis," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 358-367.
    36. Nazif Durmaz, 2023. "Closed-End Fund Discounts and Economic Policy Uncertainty," JRFM, MDPI, vol. 16(3), pages 1-29, March.
    37. Mohsen Bahmani‐Oskooee & Majid Maki Nayeri, 2018. "Policy Uncertainty and the Demand for Money in Australia: an Asymmetry Analysis," Australian Economic Papers, Wiley Blackwell, vol. 57(4), pages 456-469, December.
    38. Mohsen Bahmani‐Oskooee & Ridha Nouira & Sami Saafi, 2023. "Whose policy uncertainty affects commodity trade between Australia and the United States?," Australian Economic Papers, Wiley Blackwell, vol. 62(1), pages 101-123, March.
    39. Mohsen Bahmani-Oskooee & Parveen Akhtar & Sana Ullah & Muhammad Tariq Majeed, 2020. "Exchange Rate Risk and Uncertainty and Trade Flows: Asymmetric Evidence from Asia," JRFM, MDPI, vol. 13(6), pages 1-16, June.
    40. Mohsen Bahmani-Oskooee & Mir Obaidur Rahman & Mohammad Abdul Kashem, 2019. "Bangladesh’s trade partners and the J-curve: an asymmetry analysis," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(2), pages 174-189, May.
    41. Mohsen Bahmani-Oskooee & Nazif Durmaz, 2021. "Exchange rate volatility and Turkey–EU commodity trade: an asymmetry analysis," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(2), pages 429-482, May.
    42. Mohsen Bahmani-Oskooee & Majid Maki Nayeri, 2020. "Policy uncertainty and consumption in G7 countries: An asymmetry analysis," International Economics, CEPII research center, issue 163, pages 101-113.
    43. Mohsen Bahmani-Oskooee & Ahmed Usman & Sana Ullah, 2023. "Asymmetric Impact of Exchange Rate Volatility on Commodity Trade Between Pakistan and China," Global Business Review, International Management Institute, vol. 24(3), pages 510-534, June.
    44. M Bahmani-Oskooee & R Nouira & S Saafi, 2021. "US Export Earnings from and Import Payments to German Industries: Role of the Real Exchange Rate and Asymmetry," Economic Issues Journal Articles, Economic Issues, vol. 26(1), pages 57-73, March.
    45. Bahmani-Oskooee, Mohsen & Harvey, Hanafiah & Halicioglu, Ferda, 2021. "Does the real exchange rate play any role in the trade between Mexico and Canada? An asymmetric analysis," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 1-21.
    46. Mohsen Bahmani-Oskooee & Huseyin Karamelikli, 2021. "Asymmetric J-curve: evidence from UK-German commodity trade," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 1029-1081, November.
    47. Mohsen Bahmani-Oskooee & Augustine C. Arize, 2020. "On the Asymmetric Effects of Exchange Rate Volatility on Trade Flows: Evidence from Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(4), pages 913-939, March.
    48. Mohsen Bahmani-Oskooee & Augustine C. Arize, 2019. "The Sensitivity of U.S. Inpayments and Outpayments to Real Exchange Rate Changes: Asymmetric Evidence From Africa," International Economic Journal, Taylor & Francis Journals, vol. 33(3), pages 455-472, July.
    49. Xu, Jia & Bahmani-Oskooee, Mohsen & Karamelikli, Huseyin, 2022. "On the asymmetric effects of exchange rate uncertainty on China’s bilateral trade with its major partners," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 653-669.
    50. Xu, Jia & Bahmani-Oskooee, Mohsen & Karamelikli, Huseyin, 2022. "China’s trade in services and asymmetric J-curve," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 204-210.
    51. Mohsen Bahmani-Oskooee & Amirhossein Mohammadian, 2019. "Who benefits from euro depreciation in the euro zone?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(3), pages 577-595, August.
    52. Mohsen Bahmani-Oskooee & Sujata Saha, 2020. "Exchange rate risk and commodity trade between U.S. and India: an asymmetry analysis," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 25(4), pages 675-695, October.
    53. Bahmani-Oskooee, Mohsen & Hadj Amor, Thouraya & Maki Nayeri, Majid & Niroomand, Farhang, 2019. "On the link between real effective value of Tunisia’s Dinar and its sectoral trade with the rest of the world: New evidence from asymmetry analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 111-118.
    54. Mohsen Bahmani‐Oskooee & Ridha Nouira & Sami Saafi, 2023. "Whose policy uncertainty affects trade flows between Japan and the U.S.?," Australian Economic Papers, Wiley Blackwell, vol. 62(3), pages 457-485, September.
    55. Mohsen Bahmani-Oskooee & Hadiseh Fariditavana, 2020. "Asymmetric cointegration and the J-curve: new evidence from commodity trade between the U.S. and Canada," International Economics and Economic Policy, Springer, vol. 17(2), pages 427-482, May.
    56. Mohsen Bahmani-Oskooee & Niloy Bose & Yun Zhang, 2018. "Asymmetric Cointegration, Nonlinear ARDL, and the J-Curve: A Bilateral Analysis of China and Its 21 Trading Partners," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(13), pages 3131-3151, October.
    57. Bahmani-Oskooee, Mohsen & Nouira, Ridha, 2021. "U.S.-German commodity trade and the J-curve: New evidence from asymmetry analysis," Economic Systems, Elsevier, vol. 45(2).
    58. Mohsen Bahmani-Oskooee & Muhammad Ali Nasir, 2020. "Asymmetric J-curve: evidence from industry trade between U.S. and U.K," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2679-2693, May.
    59. Mohsen Bahmani-Oskooee & Ridha Nouira, 2020. "On the impact of exchange rate volatility on Tunisia’s trade with 16 partners: an asymmetry analysis," Economic Change and Restructuring, Springer, vol. 53(3), pages 357-378, August.

  27. Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2015. "US inflation dynamics on long-range data," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3874-3890, August.
    See citations under working paper version above.
  28. Periklis Gogas & Theophilos Papadimitriou & Maria Matthaiou & Efthymia Chrysanthidou, 2015. "Yield Curve and Recession Forecasting in a Machine Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 635-645, April.
    See citations under working paper version above.
  29. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    See citations under working paper version above.
  30. Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2014. "Public debt and private consumption in OECD countries," The Journal of Economic Asymmetries, Elsevier, vol. 11(C), pages 1-7.
    See citations under working paper version above.
  31. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2014. "Forecasting bank credit ratings," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 15(2), pages 195-209, March.
    See citations under working paper version above.
  32. Apostolos Serletis & Periklis Gogas, 2014. "Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(1), pages 229-241, February.
    See citations under working paper version above.
  33. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.

    Cited by:

    1. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    2. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    3. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    4. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    5. F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
    6. Wang, Delu & Wang, Yadong & Song, Xuefeng & Liu, Yun, 2018. "Coal overcapacity in China: Multiscale analysis and prediction," Energy Economics, Elsevier, vol. 70(C), pages 244-257.
    7. Emilio, Colombo & Gianfranco, Forte & Roberto, Rossignoli, 2016. "Still crazy after all these years: the returns on carry trade," Working Papers 327, University of Milano-Bicocca, Department of Economics, revised 07 Feb 2016.
    8. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    9. Yixi Xue & Jie Ren & Xiaohang Bi, 2019. "Impact of Influencing Factors on CO 2 Emissions in the Yangtze River Delta during Urbanization," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    10. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    12. Alexander Ryota Keeley, Kenichi Matsumoto, Kenta Tanaka, Yogi Sugiawan, and Shunsuke Managi, 2020. "The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    13. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    14. Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2017. "Carry trade returns with Support Vector Machines," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1705, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    15. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
    16. Simon Pezzutto & Reza Fazeli & Matteo De Felice & Wolfram Sparber, 2016. "Future development of the air-conditioning market in Europe: an outlook until 2020," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(6), pages 649-669, November.
    17. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    18. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    19. Mengistu, Mulu Getachew & Simane, Belay & Eshete, Getachew & Workneh, Tilahun Seyoum, 2016. "Factors affecting households' decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia," Renewable Energy, Elsevier, vol. 93(C), pages 215-227.
    20. Jikhan Jeong, 2020. "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers pje208, Job Market Papers.
    21. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    22. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    23. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    24. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    25. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
    26. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    27. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    28. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
    29. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).

  34. Apostolos Serletis & Khandokar Istiak & Periklis Gogas, 2013. "Interest Rates, Leverage, and Money," Open Economies Review, Springer, vol. 24(1), pages 51-78, February.

    Cited by:

    1. Silvia Bressan, 2017. "A Short Note on the Funding of Investment Firms Across the Crisis: Did the Turmoil Bring Changes?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(1), pages 1-3.
    2. Istiak, Khandokar & Serletis, Apostolos, 2020. "Risk, uncertainty, and leverage," Economic Modelling, Elsevier, vol. 91(C), pages 257-273.
    3. Apostolos Serletis & Dennis Nsafoah, "undated". "International Monetary Policy Spillovers," Working Papers 2018-06, Department of Economics, University of Calgary, revised 30 Jun 2018.
    4. Fleissig, Adrian R. & Jones, Barry E., 2015. "The impact of commercial sweeping on the demand for monetary assets during the Great Recession," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 412-422.
    5. Benjamin Miranda Tabak & Tito Belchior Silva Moreira & Dimas Mateus Fazio & André Luiz Cordeiro Cavalcanti & George Henrrique de Moura Cunha, 2016. "Monetary Expansion and the Banking Lending Channel," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-11, October.
    6. Istiak, Khandokar & Serletis, Apostolos, 2016. "A Note On Leverage And The Macroeconomy," Macroeconomic Dynamics, Cambridge University Press, vol. 20(1), pages 429-445, January.
    7. Karl Pinno & Apostolos Serletis, "undated". "Money, Velocity, and the Stock Market," Working Papers 2016-33, Department of Economics, University of Calgary, revised 06 Jun 2016.
    8. Apostolos Serletis & Periklis Gogas, 2014. "Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(1), pages 229-241, February.
    9. Anderson, Richard G. & Duca, John V. & Fleissig, Adrian R. & Jones, Barry E., 2019. "New monetary services (Divisia) indexes for the post-war U.S," Journal of Financial Stability, Elsevier, vol. 42(C), pages 3-17.
    10. Apostolos Serletis & Khandokar Istiak, 2018. "Broker-dealer Leverage and the Stock Market," Open Economies Review, Springer, vol. 29(2), pages 215-222, April.
    11. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.
    12. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    13. Istiak, Khandokar, 2019. "The nature of shadow bank leverage shocks on the macroeconomy," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).

  35. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.
    See citations under working paper version above.
  36. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras & John C. Mourmouris, 2013. "Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 3(1/2), pages 83-90.
    See citations under working paper version above.
  37. Periklis Gogas, 2013. "Business cycle synchronisation in the European Union: The effect of the common currency," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(1), pages 1-14.
    See citations under working paper version above.
  38. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    See citations under working paper version above.
  39. Apostolos Serletis & Anastasios Malliaris & Melvin Hinich & Periklis Gogas, 2012. "Episodic Nonlinearity in Leading Global Currencies," Open Economies Review, Springer, vol. 23(2), pages 337-357, April.
    See citations under working paper version above.
  40. Dionisios Chionis & Periklis Gogas & Ioannis Pragidis, 2010. "Predicting European Union Recessions in the Euro Era: The Yield Curve as a Forecasting Tool of Economic Activity," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 16(1), pages 1-10, February.
    See citations under working paper version above.
  41. Cajueiro, Daniel O. & Gogas, Periklis & Tabak, Benjamin M., 2009. "Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 50-57, March.

    Cited by:

    1. He, Ling-Yun & Qian, Wen-Bin, 2012. "A Monte Carlo simulation to the performance of the R/S and V/S methods—Statistical revisit and real world application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(14), pages 3770-3782.
    2. Zhu, Chaowei & Zhang, Fan & Zhang, Yuling, 2023. "Revisiting financial opening and financial development: A regulation heterogeneity perspective," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 181-197.
    3. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    4. Christian Rudolf RICHTER & Bachar FAKHRY, 2016. "Testing the Efficiency of the GIPS Sovereign Debt Markets using an Asymmetrical Volatility Test," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 524-535, September.
    5. Stosic, Dusan & Stosic, Darko & de Mattos Neto, Paulo S.G. & Stosic, Tatijana, 2019. "Multifractal characterization of Brazilian market sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 956-964.
    6. Alam, Nafis & Arshad, Shaista & Rizvi, Syed Aun R., 2016. "Do Islamic stock indices perform better than conventional counterparts? An empirical investigation of sectoral efficiency," Review of Financial Economics, Elsevier, vol. 31(C), pages 108-114.
    7. Chenyu Han & Yiming Wang & Yingying Xu, 2019. "Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash," Sustainability, MDPI, vol. 11(6), pages 1-15, March.
    8. Lamia SEBAI, 2022. "Intégration et efficience des marchés locaux et régionaux pendant la crise financière," Journal of Academic Finance, RED research unit, university of Gabes, Tunisia, vol. 13(1), pages 43-61, June.
    9. Haider Ali & Faheem Aslam & Paulo Ferreira, 2021. "Modeling Dynamic Multifractal Efficiency of US Electricity Market," Energies, MDPI, vol. 14(19), pages 1-16, September.
    10. Stavroyiannis, Stavros & Babalos, Vassilios & Bekiros, Stelios & Lahmiri, Salim & Uddin, Gazi Salah, 2019. "The high frequency multifractal properties of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 62-71.
    11. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    12. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    13. Tri Minh Nguyen, 2017. "The Impact of Foreign Investor Trading Activity on Vietnamese Stock Market," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 9(1), pages 109-118, February.
    14. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    15. Ning, Ye & Han, Chenyu & Wang, Yiming, 2018. "The multifractal properties of Euro and Pound exchange rates and comparisons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 578-587.
    16. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    17. Xiao, Di & Wang, Jun, 2021. "Attitude interaction for financial price behaviours by contact system with small-world network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    18. Bariviera, A.F. & Guercio, M. Belén & Martinez, Lisana B., 2012. "A comparative analysis of the informational efficiency of the fixed income market in seven European countries," Economics Letters, Elsevier, vol. 116(3), pages 426-428.
    19. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "The influence of trading volume on market efficiency: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 259-265.
    20. Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    21. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    22. Guo, Yaoqi & Yao, Shanshan & Cheng, Hui & Zhu, Wensong, 2020. "China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods," Resources Policy, Elsevier, vol. 68(C).
    23. Nawaz, Nasreen, 2019. "Efficiency on the Dynamic Adjustment Path in a Financial Market," MPRA Paper 118271, University Library of Munich, Germany, revised 04 Jun 2020.
    24. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    25. Rizvi, Syed Aun R. & Arshad, Shaista & Alam, Nafis, 2018. "A tripartite inquiry into volatility-efficiency-integration nexus - case of emerging markets," Emerging Markets Review, Elsevier, vol. 34(C), pages 143-161.
    26. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
    27. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Espinosa-Paredes, Gilberto, 2012. "Is the US stock market becoming weakly efficient over time? Evidence from 80-year-long data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5643-5647.
    28. Yarovaya, Larisa & Brzeszczyński, Janusz & Goodell, John W. & Lucey, Brian & Lau, Chi Keung Marco, 2022. "Rethinking financial contagion: Information transmission mechanism during the COVID-19 pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    29. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    30. Uddin, Gazi Salah & Hernandez, Jose Areola & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2018. "Time-varying evidence of efficiency, decoupling, and diversification of conventional and Islamic stocks," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 167-180.
    31. Yan Meng & Lingyun Xiong & Lijuan Xiao & Min Bai, 2023. "The effect of overseas investors on local market efficiency: evidence from the Shanghai/Shenzhen–Hong Kong Stock Connect," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-32, December.
    32. Arshad, Shaista & Rizvi, Syed Aun R., 2015. "The troika of business cycle, efficiency and volatility. An East Asian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 158-170.
    33. Stavroyiannis, S. & Makris, I. & Nikolaidis, V., 2010. "Non-extensive properties, multifractality, and inefficiency degree of the Athens Stock Exchange General Index," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 19-24, January.
    34. Navaz Naghavi & Muhammad Shujaat Mubarik & Devinder Kaur, 2018. "Financial Liberalization And Stock Market Efficiency: Measuring The Threshold Effects Of Governance," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-24, December.
    35. Wang, Yudong & Wu, Chongfeng & Pan, Zhiyuan, 2011. "Multifractal detrending moving average analysis on the US Dollar exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3512-3523.
    36. Arshad, Shaista & Rizvi, Syed Aun R. & Ghani, Gairuzazmi Mat & Duasa, Jarita, 2016. "Investigating stock market efficiency: A look at OIC member countries," Research in International Business and Finance, Elsevier, vol. 36(C), pages 402-413.
    37. Arjoon, Vaalmikki, 2016. "Microstructures, financial reforms and informational efficiency in an emerging market," Research in International Business and Finance, Elsevier, vol. 36(C), pages 112-126.
    38. Rizwan Khalid & Choudhry Tanveer Shehzad & Bushra Naqvi, 2023. "Impact of capital account liberalization on stock market crashes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3700-3726, October.
    39. Ali, Amjad, 2022. "Financial Liberalization, Institutional Quality and Economic Growth Nexus: Panel Analysis of African Countries," MPRA Paper 116329, University Library of Munich, Germany, revised 2022.
    40. Al Janabi, Mazin A.M. & Hatemi-J, Abdulnasser & Irandoust, Manuchehr, 2010. "An empirical investigation of the informational efficiency of the GCC equity markets: Evidence from bootstrap simulation," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 47-54, January.
    41. Caihong Xu & Dong Zhang, 2019. "Market openness and market quality in gold markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(3), pages 384-401, March.
    42. Liu, Li & Wang, Yudong & Wan, Jieqiu, 2010. "Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality," International Review of Financial Analysis, Elsevier, vol. 19(4), pages 237-241, September.
    43. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    44. Bariviera, Aurelio F., 2021. "One model is not enough: Heterogeneity in cryptocurrencies’ multifractal profiles," Finance Research Letters, Elsevier, vol. 39(C).
    45. Brian M Lucey & Cal Muckley, 2011. "Robust Global Stock Market Interdependencies," The Institute for International Integration Studies Discussion Paper Series iiisdp353, IIIS.
    46. da Silva Filho, Antônio Carlos & Maganini, Natália Diniz & de Almeida, Eduardo Fonseca, 2018. "Multifractal analysis of Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 954-967.
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    61. Yijie Li & Jianghui Liu & Haizhi Wang & Peng Wang, 2021. "Stock market liberalization, foreign institutional investors, and informational efficiency of stock prices: Evidence from an emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5451-5471, October.
    62. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
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    66. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
    67. Wang, Yudong & Liu, Li & Gu, Rongbao & Cao, Jianjun & Wang, Haiyan, 2010. "Analysis of market efficiency for the Shanghai stock market over time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1635-1642.
    68. Aurelio Fernández Bariviera & M. Belén Guercio & Lisana B. Martinez, 2014. "Informational Efficiency in Distressed Markets: The Case of European Corporate Bonds," The Economic and Social Review, Economic and Social Studies, vol. 45(3), pages 349-369.
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  42. Periklis Gogas & Apostolos Serletis, 2009. "Forecasting in inefficient commodity markets," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 36(4), pages 383-392, September.

    Cited by:

    1. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2022. "Forecasting Bitcoin Spikes: A GARCH-SVM Approach," Forecasting, MDPI, vol. 4(4), pages 1-15, September.
    2. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.

  43. Apostolos Serletis & Periklis Gogas, 2007. "The Feldstein‐Horioka puzzle in an ARIMA framework," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 34(3), pages 194-210, August.

    Cited by:

    1. Mohsen Bahmani-Oskooee & Rajarshi Mitra, 2009. "The J-Curve at the industry level: evidence from U.S.-India trade," Economics Bulletin, AccessEcon, vol. 29(2), pages 1520-1529.
    2. Mohsen Bahmani‐Oskooee & Scott W. Hegerty & Altin Tanku, 2010. "The Black‐Market Exchange Rate Versus The Official Rate: Which Rate Fosters The Adjustment Speed In The Monetarist Model?," Manchester School, University of Manchester, vol. 78(6), pages 725-738, December.
    3. Chu, Kam Hon, 2012. "The Feldstein-Horioka Puzzle and Spurious Ratio Correlation," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 292-309.
    4. Mohsen Bahmani-Oskooee & Marzieh Bolhasani, 2011. "How Sensitive is U.S.-Canadian Trade to the Exchange Rate: Evidence from Industry Data," Open Economies Review, Springer, vol. 22(1), pages 53-91, February.

  44. Serletis, Apostolos & Gogas, Periklis, 2004. "Long-horizon regression tests of the theory of purchasing power parity," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1961-1985, August.

    Cited by:

    1. Saadet Kasman & Adnan Kasman & Duygu Ayhan, 2010. "Testing the Purchasing Power Parity Hypothesis for the New Member and Candidate Countries of the European Union: Evidence from Lagrange Multiplier Unit Root Tests with Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 53-65, March.
    2. Meher Manzur, 2018. "Exchange rate economics is always and everywhere controversial," Applied Economics, Taylor & Francis Journals, vol. 50(3), pages 216-232, January.
    3. Ata Assaf, 2006. "Nonlinear Trend Stationarity in Real Exchange Rates: Evidence from Nonlinear ADF tests," Annals of Economics and Finance, Society for AEF, vol. 7(2), pages 283-294, November.
    4. Zenon Wisniewski, 2021. "Long-Term Relationship Between Prices and Exchange Rates," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 63-86.
    5. Sarno, Lucio & Valente, Giorgio, 2006. "Deviations from purchasing power parity under different exchange rate regimes: Do they revert and, if so, how?," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3147-3169, November.
    6. Ho, Catherine S.F. & Ariff, M., 2012. "Time to equilibrium in exchange rates: G-10 and Eastern European economies," Global Finance Journal, Elsevier, vol. 23(2), pages 94-107.
    7. Frederick H. Wallace & Gary L. Shelley, 2005. "An Alternative Test of Purchasing Power Parity," International Finance 0502009, University Library of Munich, Germany.
    8. Jiménez-Méndez, Edgar Ricardo & Aguilera Peña, Nicolás, 2021. "Aplicación de la hipótesis de paridad de poder adquisitivo en el pronóstico de la tasa de cambio del peso colombiano contra el dólar estadounidense || Application of the purchasing power parity hypoth," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 29-48, December.
    9. Georgios Chortareas & George Kapetanios, 2004. "Getting PPP Right: Identifying Mean Reverting Real Exchange Rates in Panels," Money Macro and Finance (MMF) Research Group Conference 2004 32, Money Macro and Finance Research Group.
    10. Shiu‐Sheng Chen & Yu‐Hsi Chou, 2010. "Exchange Rates and Fundamentals: Evidence from Long‐Horizon Regression Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 63-88, February.
    11. Marc Audi & Amjad Ali, 2023. "The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 292-305, May.
    12. Serletis, Apostolos & Shahmoradi, Asghar, 2007. "Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1437-1444.
    13. Kutan, Ali M. & Zhou, Su, 2015. "PPP may hold better than you think: Smooth breaks and non-linear mean reversion in real effective exchange rates," Economic Systems, Elsevier, vol. 39(2), pages 358-366.
    14. Catherine Ho & M. Ariff, 2011. "Sticky prices and time to equilibrium: evidence from Asia-Pacific trade-related economies," Applied Economics, Taylor & Francis Journals, vol. 43(21), pages 2851-2861.
    15. Zhou, Su & Kutan, Ali M., 2011. "Is the evidence for PPP reliable? A sustainability examination of the stationarity of real exchange rates," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2479-2490, September.
    16. Mei Qiu & Pinfold & Rose, 2015. "A currency preferential approach to international equity investment," Applied Economics, Taylor & Francis Journals, vol. 47(49), pages 5247-5261, October.

  45. Apostolos Serletis & Periklis Gogas, 1999. "The North American Natural Gas Liquids Markets are Chaotic," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 83-103.
    See citations under working paper version above.
  46. Serletis, Apostolos & Gogas, Periklis, 1997. "Chaos in East European black market exchange rates," Research in Economics, Elsevier, vol. 51(4), pages 359-385, December.
    See citations under working paper version above.

Chapters

  1. Theophilos Papadimitriou & Periklis Gogas & Georgios Antonios Sarantitis, 2014. "European Business Cycle Synchronization: A Complex Network Perspective," Springer Optimization and Its Applications, in: Valery A. Kalyagin & Panos M. Pardalos & Themistocles M. Rassias (ed.), Network Models in Economics and Finance, edition 127, pages 265-275, Springer.
    See citations under working paper version above.
  2. Apostolos Serletis & Periklis Gogas, 2007. "The North American Natural Gas Liquids Markets are Chaotic," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 17, pages 225-244, World Scientific Publishing Co. Pte. Ltd..
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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