IDEAS home Printed from https://ideas.repec.org/e/c/pfa92.html
   My authors  Follow this author

Dean Fantazzini

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.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

Working papers

  1. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    2. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.

  2. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    2. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    3. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.
    4. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.

  3. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.

    Cited by:

    1. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.

  4. Fantazzini, Dean & Pushchelenko, Julia & Mironenkov, Alexey & Kurbatskii, Alexey, 2021. "Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg," MPRA Paper 110452, University Library of Munich, Germany.

    Cited by:

    1. Alina Sîrbu & Diletta Goglia & Jisu Kim & Paul Maximilian Magos & Laura Pollacci & Spyridon Spyratos & Giulio Rossetti & Stefano Maria Iacus, 2024. "International mobility between the UK and Europe around Brexit: a data-driven study," Journal of Computational Social Science, Springer, vol. 7(2), pages 1451-1482, October.
    2. Bronitsky, Georgy & Vakulenko, Elena, 2024. "Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 78-101.
    3. Bert Leysen & Pieter-Paul Verhaeghe, 2023. "Searching for migration: estimating Japanese migration to Europe with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4603-4631, October.
    4. Tongzheng Pu & Chongxing Huang & Jingjing Yang & Ming Huang, 2023. "Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    5. Nathan Wycoff & Lisa O. Singh & Ali Arab & Katharine M. Donato & Helge Marahrens, 2024. "The digital trail of Ukraine’s 2022 refugee exodus," Journal of Computational Social Science, Springer, vol. 7(2), pages 2147-2193, October.

  5. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    3. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.
    4. Vittorio Astarita, 2023. "Risks and opportunities in arbitrage and market-making in blockchain-based currency markets. Part 1 : Risks," Papers 2304.08590, arXiv.org.
    5. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    6. Fatih Ecer & Tolga Murat & Hasan Dinçer & Serhat Yüksel, 2024. "A fuzzy BWM and MARCOS integrated framework with Heronian function for evaluating cryptocurrency exchanges: a case study of Türkiye," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.

  6. Fantazzini, Dean & Kolesnikova, Anna, 2021. "Asymmetry and hysteresis in the Russian gasoline market: the rationale for green energy exports," MPRA Paper 109297, University Library of Munich, Germany.

    Cited by:

    1. Kyungsoo Cha & Chul-Yong Lee, 2023. "Rockets and Feathers in the Gasoline Market: Evidence from South Korea," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

  7. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper 102315, University Library of Munich, Germany.

    Cited by:

    1. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    2. Nelson Mileu & Nuno M. Costa & Eduarda M. Costa & André Alves, 2022. "Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data," Data, MDPI, vol. 7(8), pages 1-17, July.
    3. Lidia Betcheva & Feryal Erhun & Antoine Feylessoufi & Peter Fryers & Paulo Gonçalves & Houyuan Jiang & Paul Kattuman & Tom Pape & Anees Pari & Stefan Scholtes & Carina Tyrrell, 2024. "An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England," Interfaces, INFORMS, vol. 54(6), pages 500-516, November.
    4. Schneider, Tim & Meub, Lukas & Bizer, Kilian, 2021. "Consumer information in a market for expert services: Experimental evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    5. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

  8. Fantazzini, Dean & Kolodin, Nikita, 2020. "Does the hashrate affect the bitcoin price?," MPRA Paper 103812, University Library of Munich, Germany.

    Cited by:

    1. David Cerezo Sánchez, 2022. "Pravuil: Global Consensus for a United World," FinTech, MDPI, vol. 1(4), pages 1-20, October.
    2. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    3. Juraj Fabus & Iveta Kremenova & Natalia Stalmasekova & Terezia Kvasnicova-Galovicova, 2024. "An Empirical Examination of Bitcoin’s Halving Effects: Assessing Cryptocurrency Sustainability within the Landscape of Financial Technologies," JRFM, MDPI, vol. 17(6), pages 1-23, May.
    4. King, Juan C. & Dale, Roberto & Amigó, José M., 2024. "Blockchain metrics and indicators in cryptocurrency trading," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    5. Pardis Roozkhosh & Alireza Pooya, 2024. "Dynamic Analysis of Bitcoin Price Under Market News and Sentiments and Government Support Policies," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1163-1198, August.
    6. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    7. Kubal, Jan & Kristoufek, Ladislav, 2022. "Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system," International Review of Financial Analysis, Elsevier, vol. 84(C).
    8. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    9. Carbó, José Manuel & Gorjón, Sergio, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 123-140.
    10. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.
    11. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    12. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    13. Marthinsen, John E. & Gordon, Steven R., 2022. "The price and cost of bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 280-288.
    14. Clément Landormy, 2024. "An inquiry of Bitcoin price formation: Evidence from Linear and Nonlinear ARDL Frameworks, 2017-2018," Working Papers of BETA 2024-31, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    15. David Cerezo S'anchez, 2021. "Pravuil: Global Consensus for a United World," Papers 2105.10464, arXiv.org.
    16. Juan C. King & Roberto Dale & Jos'e M. Amig'o, 2024. "Blockchain Metrics and Indicators in Cryptocurrency Trading," Papers 2403.00770, arXiv.org.

  9. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," MPRA Paper 95992, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    2. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    3. Makushkin, Mikhail & Lapshin, Victor, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.
    4. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  10. Bazhenov, Timofey & Fantazzini, Dean, 2019. "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility," MPRA Paper 93544, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    2. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," MPRA Paper 95992, University Library of Munich, Germany.
    3. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  11. Fantazzini, Dean & Zimin, Stephan, 2019. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," MPRA Paper 95988, University Library of Munich, Germany.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Müller, Fernanda Maria & Santos, Samuel Solgon & Gössling, Thalles Weber & Righi, Marcelo Brutti, 2022. "Comparison of risk forecasts for cryptocurrencies: A focus on Range Value at Risk," Finance Research Letters, Elsevier, vol. 48(C).
    3. Tim Schmitz & Ingo Hoffmann, 2020. "Re-evaluating cryptocurrencies' contribution to portfolio diversification -- A portfolio analysis with special focus on German investors," Papers 2006.06237, arXiv.org, revised Aug 2020.
    4. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    5. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    6. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.
    7. Nguyen, An Pham Ngoc & Mai, Tai Tan & Bezbradica, Marija & Crane, Martin, 2023. "Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    8. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.
    9. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    10. Giancarlo Giudici & Alistair Milne & Dmitri Vinogradov, 2020. "Cryptocurrencies: market analysis and perspectives," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 1-18, March.
    11. Nora CHIRIȚĂ & Ionuț NICA, 2020. "An approach to the use of cryptocurrencies in Romania using data mining technique," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(622), S), pages 5-20, Spring.
    12. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.

  12. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask," MPRA Paper 71946, University Library of Munich, Germany, revised 2016.

    Cited by:

    1. Schilling, Linda & Uhlig, Harald, 2019. "Some simple bitcoin economics," Journal of Monetary Economics, Elsevier, vol. 106(C), pages 16-26.
    2. Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2019. "Metcalfe's law and log-period power laws in the cryptocurrencies market," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-26.
    3. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2020. "Equilibrium Bitcoin Pricing," EconPol Working Paper 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Guglielmo Maria Caporale & Timur Zekokh, 2018. "Modelling Volatility of Cryptocurrencies Using Markov-Switching Garch Models," CESifo Working Paper Series 7167, CESifo.
    5. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-04250269, HAL.
    6. Juneman Abraham & Dian Utami Sutiksno & Nuning Kurniasih & Ari Warokka, 2019. "Acceptance and Penetration of Bitcoin: The Role of Psychological Distance and National Culture," SAGE Open, , vol. 9(3), pages 21582440198, July.
    7. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    8. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    9. Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2019. "Metcalfe's law and herding behaviour in the cryptocurrencies market," Economics Discussion Papers 2019-16, Kiel Institute for the World Economy (IfW Kiel).
    10. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    11. Viviane Naimy & Omar Haddad & Gema Fernández-Avilés & Rim El Khoury, 2021. "The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.
    12. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    13. Sofoklis Vogiazas & Constantinos Alexiou, 2019. "Bitcoin: The Road to Hell Is Paved With Good Promises," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 48(1), February.
    14. Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2018. "Cryptocurrencies, Metcalfe's law and LPPL models," IRTG 1792 Discussion Papers 2018-056, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Zura Kakushadze & Jim Kyung-Soo Liew, 2018. "CryptoRuble: From Russia with Love," Papers 1801.05760, arXiv.org.

  13. Fantazzini, Dean, 2016. "The Oil Price Crash in 2014/15: Was There a (Negative) Financial Bubble?," MPRA Paper 72094, University Library of Munich, Germany.

    Cited by:

    1. Theodosios Perifanis, 2019. "Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods," Energies, MDPI, vol. 12(14), pages 1-16, July.
    2. Gharib, Cheima & Mefteh-Wali, Salma & Jabeur, Sami Ben, 2021. "The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets," Finance Research Letters, Elsevier, vol. 38(C).
    3. Yonghong Jiang & Gengyu Tian & Bin Mo, 2020. "Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-26, December.
    4. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    5. Semeyutin, Artur & Gozgor, Giray & Lau, Chi Keung Marco & Xu, Bing, 2021. "Effects of idiosyncratic jumps and co-jumps on oil, gold, and copper markets," Energy Economics, Elsevier, vol. 104(C).
    6. Papastamatiou, Konstantinos & Karakasidis, Theodoros, 2022. "Bubble detection in Greek Stock Market: A DS-LPPLS model approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    7. Lei Ming & Yao Shen & Shenggang Yang & Minyi Dong, 2022. "Contagion or flight‐to‐quality? The linkage between oil price and the US dollar based on the local Gaussian approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 722-750, April.
    8. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    9. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    10. Gomis-Porqueras, Pedro & Shi, Shuping & Tan, David, 2020. "Gold as a Financial Instrument," MPRA Paper 102782, University Library of Munich, Germany.
    11. Potrykus, Marcin, 2023. "Investing in wine, precious metals and G-7 stock markets – A co-occurrence analysis for price bubbles," International Review of Financial Analysis, Elsevier, vol. 87(C).
    12. Figuerola-Ferretti, Isabel & McCrorie, J. Roderick & Paraskevopoulos, Ioannis, 2020. "Mild explosivity in recent crude oil prices," Energy Economics, Elsevier, vol. 87(C).
    13. Tanin, Tauhidul Islam & Hasanov, Akram Shavkatovich & Shaiban, Mohammed Sharaf Mohsen & Brooks, Robert, 2022. "Risk transmission from the oil market to Islamic and conventional banks in oil-exporting and oil-importing countries," Energy Economics, Elsevier, vol. 115(C).
    14. Pham, Linh, 2019. "Do all clean energy stocks respond homogeneously to oil price?," Energy Economics, Elsevier, vol. 81(C), pages 355-379.
    15. Caravello, Tomas E. & Psaradakis, Zacharias & Sola, Martin, 2023. "Rational bubbles: Too many to be true?," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    16. Berk, Istemi & Çam, Eren, 2020. "The shift in global crude oil market structure: A model-based analysis of the period 2013–2017," Energy Policy, Elsevier, vol. 142(C).
    17. Khan, Muhammad Imran & Yasmeen, Tabassam & Shakoor, Abdul & Khan, Niaz Bahadur & Muhammad, Riaz, 2017. "2014 oil plunge: Causes and impacts on renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 609-622.
    18. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Pawe{l} O'swic{e}cimka & Marek Stanuszek, 2018. "Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017," Papers 1812.08548, arXiv.org, revised Jun 2019.
    19. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    20. Khan, Khalid & Su, Chi Wei & Khurshid, Adnan, 2022. "Do booms and busts identify bubbles in energy prices?," Resources Policy, Elsevier, vol. 76(C).
    21. Skrobotov Anton, 2023. "Testing for explosive bubbles: a review," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-26, January.
    22. Coskun, Merve & Taspinar, Nigar, 2022. "Volatility spillovers between Turkish energy stocks and fossil fuel energy commodities based on time and frequency domain approaches," Resources Policy, Elsevier, vol. 79(C).
    23. Chen, Shyh-Wei & Wu, An-Chi, 2018. "Is there a bubble component in government debt? New international evidence," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 467-486.
    24. Huszár, Zsuzsa R. & Kotró, Balázs B. & Tan, Ruth S.K., 2023. "Dynamic volatility transfer in the European oil and gas industry," Energy Economics, Elsevier, vol. 127(PA).
    25. Haykir, Ozkan & Yagli, Ibrahim & Aktekin Gok, Emine Dilara & Budak, Hilal, 2022. "Oil price explosivity and stock return: Do sector and firm size matter?," Resources Policy, Elsevier, vol. 78(C).
    26. Potrykus, Marcin, 2023. "Price bubbles in commodity market – A single time series and panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 110-117.
    27. Anton Skrobotov, 2022. "Testing for explosive bubbles: a review," Papers 2207.08249, arXiv.org.
    28. Wang, Tiantian & Wu, Fei & Dickinson, David & Zhao, Wanli, 2024. "Energy price bubbles and extreme price movements: Evidence from China's coal market," Energy Economics, Elsevier, vol. 129(C).
    29. Peter C. B. Phillips & Shuping Shi, 2019. "Detecting Financial Collapse and Ballooning Sovereign Risk," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1336-1361, December.
    30. S. Boubaker & Liu, Z. & Zhan, Y., 2021. "Risk management for crude oil futures: an optimal stopping-timing approach," Post-Print hal-03323674, HAL.
    31. El Montasser, Ghassen & Malek Belhoula, Mohamed & Charfeddine, Lanouar, 2023. "Co-explosivity versus leading effects: Evidence from crude oil and agricultural commodities," Resources Policy, Elsevier, vol. 81(C).
    32. Ansari, Dawud, 2017. "OPEC, Saudi Arabia, and the shale revolution: Insights from equilibrium modelling and oil politics," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 111, pages 166-178.
    33. Pastor, Daniel J. & Ewing, Bradley T., 2022. "Is there evidence of mild explosive behavior in Alaska North Slope crude oil prices?," Energy Economics, Elsevier, vol. 114(C).
    34. Ajmi, Ahdi Noomen & Hammoudeh, Shawkat & Mokni, Khaled, 2021. "Detection of bubbles in WTI, brent, and Dubai oil prices: A novel double recursive algorithm," Resources Policy, Elsevier, vol. 70(C).
    35. Berk, Istemi & Çam , Eren, 2019. "The Shift in Global Crude Oil Market Structure: A model-based analysis of the period 2013–2017," EWI Working Papers 2019-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    36. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    37. Karin Martín-Bujack & Isabel Figuerola-Ferretti & Teresa Corzo & Ioannis Paraskevopoulos, 2022. "Building Knowledge in the Oil Market," SAGE Open, , vol. 12(1), pages 21582440211, January.
    38. Benlagha, Noureddine, 2020. "Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade," Research in International Business and Finance, Elsevier, vol. 54(C).
    39. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    40. Zhao, Zhao & Wen, Huwei & Li, Ke, 2021. "Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China," Economic Modelling, Elsevier, vol. 94(C), pages 780-788.
    41. Akcora, Begum & Kandemir Kocaaslan, Ozge, 2023. "Price bubbles in the European natural gas market between 2011 and 2020," Resources Policy, Elsevier, vol. 80(C).
    42. Merve Coskun, 2023. "Dynamic correlations and volatility spillovers between subsectoral clean‐energy stocks and commodity futures markets: A hedging perspective," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1727-1749, December.
    43. Christos Floros & Georgios Galyfianakis, 2020. "Bubbles in Crude Oil and Commodity Energy Index: New Evidence," Energies, MDPI, vol. 13(24), pages 1-11, December.
    44. Henseler, Martin & Maisonnave, Helene, 2018. "Low world oil prices: A chance to reform fuel subsidies and promote public transport? A case study for South Africa," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 45-62.
    45. Peter C.B. Phillips & Shuping Shi, 2018. "Real Time Monitoring of Asset Markets: Bubbles and Crises," Cowles Foundation Discussion Papers 2152, Cowles Foundation for Research in Economics, Yale University.
    46. Palacio-Ciro, Santiago & Vasco-Correa, Carlos Andrés, 2020. "Biofuels policy in Colombia: A reconfiguration to the sugar and palm sectors?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    47. Ye Chen & Jian Li & Qiyuan Li, 2023. "Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 910-937, August.
    48. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    49. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).
    50. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    51. Reddy, K.S. & Xie, En, 2017. "Cross-border mergers and acquisitions by oil and gas multinational enterprises: Geography-based view of energy strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 961-980.
    52. Ayben Koy, 2022. "Regime Switching Mechanism during Energy Futures Price Bubbles," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 373-382.

  14. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German Car Sales Using Google Data and Multivariate Models," MPRA Paper 67110, University Library of Munich, Germany.

    Cited by:

    1. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa," Data, MDPI, vol. 8(5), pages 1-16, April.
    2. Nymand-Andersen, Per & Pantelidis, Emmanouil, 2018. "Google econometrics: nowcasting euro area car sales and big data quality requirements," Statistics Paper Series 30, European Central Bank.
    3. Homolka, Lubor & Ngo, Vu Minh & Pavelková, Drahomíra & Le, Bach Tuan & Dehning, Bruce, 2020. "Short- and medium-term car registration forecasting based on selected macro and socio-economic indicators in European countries," Research in Transportation Economics, Elsevier, vol. 80(C).
    4. Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
    5. Yong Zhang & Miner Zhong & Nana Geng & Yunjian Jiang, 2017. "Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    6. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    7. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    8. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    9. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    10. Park, Jiyoun & Nam, Changi & Kim, Hye-jin, 2019. "Exploring the key services and players in the smart car market," Telecommunications Policy, Elsevier, vol. 43(10).
    11. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
    12. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    13. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    14. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    15. M. Elshendy & A. Fronzetti Colladon & E. Battistoni & P. A. Gloor, 2021. "Using four different online media sources to forecast the crude oil price," Papers 2105.09154, arXiv.org.
    16. Park, Jiyoun & Nam, Changi & Kim, Hye-jin & Kim, Seongcheol, 2018. "What are the relative importance of smart car utilities from consumer perspective and who will lead them?," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190334, International Telecommunications Society (ITS).
    17. Takumi Kato, 2022. "Demand Prediction in the Automobile Industry Independent of Big Data," Annals of Data Science, Springer, vol. 9(2), pages 249-270, April.
    18. VAN DER WIELEN Wouter & BARRIOS Salvador, 2020. "Fear and Employment During the COVID Pandemic: Evidence from Search Behaviour in the EU," JRC Working Papers on Taxation & Structural Reforms 2020-08, Joint Research Centre.
    19. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    20. AGARWAL Reeti & MEHROTRA Ankit, 2023. "Influence Of Online Forums On Customers’ Buying Decisions: A Machine Learning Approach," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 18(3), pages 5-23, December.
    21. Meshcheryakov, Artem & Winters, Drew B., 2022. "Retail investor attention and the limit order book: Intraday analysis of attention-based trading," International Review of Financial Analysis, Elsevier, vol. 81(C).
    22. Fantazzini, Dean & Kolodin, Nikita, 2020. "Does the hashrate affect the bitcoin price?," MPRA Paper 103812, University Library of Munich, Germany.
    23. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    24. Jolana Stejskalova, 2023. "We investigated the link between stock returns of automobile companies, Fama French factors, and behavioral attention, represented by demand for a selected car brand belonging to an automobile company," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(3), pages 202-221, March.
    25. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    26. Juan Manuel García Sánchez & Xavier Vilasís Cardona & Alexandre Lerma Martín, 2022. "Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    27. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    28. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    29. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," MPRA Paper 95992, University Library of Munich, Germany.
    30. Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

  15. Fantazziini, Dean, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US using Online Search Data," MPRA Paper 59696, University Library of Munich, Germany.

    Cited by:

    1. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    2. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    3. Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    4. Kerry Liu, 2023. "America's decoupling from China: A perspective from stock markets," Economic Affairs, Wiley Blackwell, vol. 43(1), pages 32-52, February.
    5. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    6. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).

  16. Höök, Mikael & Fantazzini, Dean & Angelantoni, André & Snowden, Simon, 2013. "Hydrocarbon liquefaction: viability as a peak oil mitigation strategy," MPRA Paper 46957, University Library of Munich, Germany.

    Cited by:

    1. Ringsmuth, Andrew K. & Landsberg, Michael J. & Hankamer, Ben, 2016. "Can photosynthesis enable a global transition from fossil fuels to solar fuels, to mitigate climate change and fuel-supply limitations?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 134-163.
    2. Zihan Liu & Ju’e Guo & Shubin Wang & Hongtao Liu, 2018. "Government incentive strategies and private capital participation in China’s Shale gas development," Applied Economics, Taylor & Francis Journals, vol. 50(1), pages 51-64, January.
    3. Capellán-Pérez, Iñigo & Mediavilla, Margarita & de Castro, Carlos & Carpintero, Óscar & Miguel, Luis Javier, 2014. "Fossil fuel depletion and socio-economic scenarios: An integrated approach," Energy, Elsevier, vol. 77(C), pages 641-666.

  17. Larsson, Simon & Fantazzini, Dean & Davidsson, Simon & Kullander, Sven & Hook, Mikael, 2013. "Reviewing electricity production cost assessments," MPRA Paper 50306, University Library of Munich, Germany.

    Cited by:

    1. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2019. "Global levelised cost of electricity from offshore wind," Energy, Elsevier, vol. 189(C).
    2. Tataraki, Kalliopi G. & Kavvadias, Konstantinos C. & Maroulis, Zacharias B., 2018. "A systematic approach to evaluate the economic viability of Combined Cooling Heating and Power systems over conventional technologies," Energy, Elsevier, vol. 148(C), pages 283-295.
    3. Burgherr, Peter & Hirschberg, Stefan, 2014. "Comparative risk assessment of severe accidents in the energy sector," Energy Policy, Elsevier, vol. 74(S1), pages 45-56.
    4. Strantzali, Eleni & Aravossis, Konstantinos, 2016. "Decision making in renewable energy investments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 885-898.
    5. Santa Catarina, Artur, 2022. "Wind power generation in Brazil: An overview about investment and scale analysis in 758 projects using the Levelized Cost of Energy," Energy Policy, Elsevier, vol. 164(C).
    6. Yuan, Jiahai & Sun, Shenghui & Zhang, Wenhua & Xiong, Minpeng, 2014. "The economy of distributed PV in China," Energy, Elsevier, vol. 78(C), pages 939-949.
    7. Spada, Matteo & Paraschiv, Florentina & Burgherr, Peter, 2018. "A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies," Energy, Elsevier, vol. 154(C), pages 277-288.
    8. Do, Truong Xuan & Lim, Young-il, 2016. "Techno-economic comparison of three energy conversion pathways from empty fruit bunches," Renewable Energy, Elsevier, vol. 90(C), pages 307-318.
    9. van den Broek, Machteld & Berghout, Niels & Rubin, Edward S., 2015. "The potential of renewables versus natural gas with CO2 capture and storage for power generation under CO2 constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1296-1322.
    10. Strantzali, Eleni & Aravossis, Konstantinos & Livanos, Georgios A., 2017. "Evaluation of future sustainable electricity generation alternatives: The case of a Greek island," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 775-787.
    11. Massarutto, Antonio & Pontoni, Federico, 2015. "Rent seizing and environmental concerns: A parametric valuation of the Italian hydropower sector," Energy Policy, Elsevier, vol. 78(C), pages 31-40.
    12. Colla, Martin & Ioannou, Anastasia & Falcone, Gioia, 2020. "Critical review of competitiveness indicators for energy projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    13. Ferreira, Ana C. & Nunes, Manuel L. & Teixeira, José C.F. & Martins, Luís A.S.B. & Teixeira, Senhorinha F.C.F., 2016. "Thermodynamic and economic optimization of a solar-powered Stirling engine for micro-cogeneration purposes," Energy, Elsevier, vol. 111(C), pages 1-17.
    14. Vu, Thang Toan & Lim, Young-Il & Song, Daesung & Mun, Tae-Young & Moon, Ji-Hong & Sun, Dowon & Hwang, Yoon-Tae & Lee, Jae-Goo & Park, Young Cheol, 2020. "Techno-economic analysis of ultra-supercritical power plants using air- and oxy-combustion circulating fluidized bed with and without CO2 capture," Energy, Elsevier, vol. 194(C).
    15. Niwagira Daniel & Juyoul Kim, 2022. "A Study on Integrating SMRs into Uganda’s Future Energy System," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    16. Epari Ritesh Patro & Teegala Srinivasa Kishore & Ali Torabi Haghighi, 2022. "Levelized Cost of Electricity Generation by Small Hydropower Projects under Clean Development Mechanism in India," Energies, MDPI, vol. 15(4), pages 1-16, February.
    17. Davidsson, Simon & Grandell, Leena & Wachtmeister, Henrik & Höök, Mikael, 2014. "Growth curves and sustained commissioning modelling of renewable energy: Investigating resource constraints for wind energy," Energy Policy, Elsevier, vol. 73(C), pages 767-776.
    18. Rozhkov, Anton, 2024. "Applying graph theory to find key leverage points in the transition toward urban renewable energy systems," Applied Energy, Elsevier, vol. 361(C).
    19. Shangfeng Han & Baosheng Zhang & Xiaoyang Sun & Song Han & Mikael Höök, 2017. "China’s Energy Transition in the Power and Transport Sectors from a Substitution Perspective," Energies, MDPI, vol. 10(5), pages 1-25, April.
    20. Zhang, Jian & Cho, Heejin & Knizley, Alta, 2016. "Evaluation of financial incentives for combined heat and power (CHP) systems in U.S. regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 738-762.
    21. Muratori, Matteo & Ledna, Catherine & McJeon, Haewon & Kyle, Page & Patel, Pralit & Kim, Son H. & Wise, Marshall & Kheshgi, Haroon S. & Clarke, Leon E. & Edmonds, Jae, 2017. "Cost of power or power of cost: A U.S. modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 861-874.
    22. Ali, Babkir, 2018. "Comparative assessment of the feasibility for solar irrigation pumps in Sudan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 413-420.
    23. Rodriguez-Sanchez, David & Rosengarten, Gary, 2015. "Improving the concentration ratio of parabolic troughs using a second-stage flat mirror," Applied Energy, Elsevier, vol. 159(C), pages 620-632.
    24. Zhao, Zhen-Yu & Chen, Yu-Long & Thomson, John Douglas, 2017. "Levelized cost of energy modeling for concentrated solar power projects: A China study," Energy, Elsevier, vol. 120(C), pages 117-127.
    25. Carneiro, Pedro & Soares dos Santos, Marco P. & Rodrigues, André & Ferreira, Jorge A.F. & Simões, José A.O. & Marques, A. Torres & Kholkin, Andrei L., 2020. "Electromagnetic energy harvesting using magnetic levitation architectures: A review," Applied Energy, Elsevier, vol. 260(C).
    26. Killinger, Sven & Mainzer, Kai & McKenna, Russell & Kreifels, Niklas & Fichtner, Wolf, 2015. "A regional optimisation of renewable energy supply from wind and photovoltaics with respect to three key energy-political objectives," Energy, Elsevier, vol. 84(C), pages 563-574.
    27. Akbi, Amine & Yassaa, Noureddine & Boudjema, Rachid & Aliouat, Boualem, 2016. "A new method for cost of renewable energy production in Algeria: Integrate all benefits drawn from fossil fuel savings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1150-1157.
    28. Rahman, Shaikh M. & Spalding-Fecher, Randall & Haites, Erik & Kirkman, Grant A., 2018. "The levelized costs of electricity generation by the CDM power projects," Energy, Elsevier, vol. 148(C), pages 235-246.
    29. García-Gusano, Diego & Espegren, Kari & Lind, Arne & Kirkengen, Martin, 2016. "The role of the discount rates in energy systems optimisation models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 56-72.
    30. Mou, Dunguo & Wang, Zining, 2022. "A systematic analysis of integrating variable wind power into Fujian power grid," Energy Policy, Elsevier, vol. 170(C).

  18. Eduardo Rossi & Dean Fantazzini, 2012. "Long memory and Periodicity in Intraday Volatility," DEM Working Papers Series 015, University of Pavia, Department of Economics and Management.

    Cited by:

    1. Fantazziini, Dean, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US using Online Search Data," MPRA Paper 59696, University Library of Munich, Germany.
    2. Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    4. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    5. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German Car Sales Using Google Data and Multivariate Models," MPRA Paper 67110, University Library of Munich, Germany.
    6. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    7. Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
    8. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    9. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    10. Chen, Zhonglu & Ye, Yong & Li, Xiafei, 2022. "Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic," Resources Policy, Elsevier, vol. 75(C).
    11. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2020. "Periodic autoregressive conditional duration," MPRA Paper 101696, University Library of Munich, Germany, revised 08 Jul 2020.
    12. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
    13. Leschinski, Christian & Sibbertsen, Philipp, 2014. "Model Order Selection in Seasonal/Cyclical Long Memory Models," Hannover Economic Papers (HEP) dp-535, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    14. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Jan 2025.
    15. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2017. "Decoupling the short- and long-term behavior of stochastic volatility," CREATES Research Papers 2017-26, Department of Economics and Business Economics, Aarhus University.
    16. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    17. Abdelhakim Aknouche & Bader Almohaimeed & Stefanos Dimitrakopoulos, 2022. "Periodic autoregressive conditional duration," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 5-29, January.
    18. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    19. Aknouche, Abdelhakim & Rabehi, Nadia, 2024. "Inspecting a seasonal ARIMA model with a random period," MPRA Paper 120758, University Library of Munich, Germany.
    20. Liu, Zhicao & Ye, Yong & Ma, Feng & Liu, Jing, 2017. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 181-188.
    21. Aknouche, Abdelhakim & Al-Eid, Eid & Demouche, Nacer, 2016. "Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models," MPRA Paper 75770, University Library of Munich, Germany, revised 19 Dec 2016.
    22. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2016. "Decoupling the short- and long-term behavior of stochastic volatility," Papers 1610.00332, arXiv.org, revised Jan 2021.
    23. Barbara Bedowska-Sojka, 2011. "The Impact of Macro News on Volatility of Stock Exchanges," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 99-110.
    24. Herrmann, Klaus & Teis, Stefan & Yu, Weijun, 2014. "Components of intraday volatility and their prediction at different sampling frequencies with application to DAX and BUND futures," FAU Discussion Papers in Economics 15/2014, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    25. Aknouche Abdelhakim & Demmouche Nacer & Dimitrakopoulos Stefanos & Touche Nassim, 2020. "Bayesian analysis of periodic asymmetric power GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-24, September.
    26. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    27. Abdelhakim Aknouche & Eid Al-Eid & Nacer Demouche, 2018. "Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models," Statistical Inference for Stochastic Processes, Springer, vol. 21(3), pages 485-511, October.
    28. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    29. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
    30. Aknouche, Abdelhakim & Demmouche, Nacer & Touche, Nassim, 2018. "Bayesian MCMC analysis of periodic asymmetric power GARCH models," MPRA Paper 91136, University Library of Munich, Germany.

  19. Fantazzini, Dean & Geraskin, Petr, 2011. "Everything You Always Wanted to Know about Log Periodic Power Laws for Bubble Modelling but Were Afraid to Ask," MPRA Paper 47869, University Library of Munich, Germany.

    Cited by:

    1. Kristoffer Pons Bertelsen, 2019. "Comparing Tests for Identification of Bubbles," CREATES Research Papers 2019-16, Department of Economics and Business Economics, Aarhus University.
    2. Demos, G. & Sornette, D., 2019. "Comparing nested data sets and objectively determining financial bubbles’ inceptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 661-675.
    3. Papastamatiou, Konstantinos & Karakasidis, Theodoros, 2022. "Bubble detection in Greek Stock Market: A DS-LPPLS model approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. Daniel Traian Pele & Miruna Mazurencu-Marinescu & Peter Nijkamp, 2013. "Herding Behaviour, Bubbles and Log Periodic Power Laws in Illiquid Stock Markets. A Case Study on the Bucharest Stock Exchange," Tinbergen Institute Discussion Papers 13-109/VIII, Tinbergen Institute.
    5. John Fry, 2014. "Bubbles, shocks and elementary technical trading strategies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(1), pages 1-13, January.
    6. Kozłowska, M. & Denys, M. & Wiliński, M. & Link, G. & Gubiec, T. & Werner, T.R. & Kutner, R. & Struzik, Z.R., 2016. "Dynamic bifurcations on financial markets," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 126-142.
    7. Guilherme DEMOS & Qunzhi ZHANG & Didier SORNETTE, 2015. "Birth or Burst of Financial Bubbles: Which One is Easier to Diagnose?," Swiss Finance Institute Research Paper Series 15-57, Swiss Finance Institute.
    8. Cifarelli, Giulio & Paesani, Paolo, 2018. "Navigating the oil bubble: A non-linear heterogeneous-agent dynamic model of futures oil pricing," MPRA Paper 90470, University Library of Munich, Germany.
    9. Shihai Dong & Yandong Wang & Yanyan Gu & Shiwei Shao & Hui Liu & Shanmei Wu & Mengmeng Li, 2020. "Predicting the turning points of housing prices by combining the financial model with genetic algorithm," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    10. Fantazzini, Dean, 2016. "The oil price crash in 2014/15: Was there a (negative) financial bubble?," Energy Policy, Elsevier, vol. 96(C), pages 383-396.
    11. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.
    12. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    13. Qunzhi Zhang & Didier Sornette & Mehmet Balcilar & Rangan Gupta & Zeynel A. Ozdemir & Hakan Yetkiner, 2016. "LPPLS Bubble Indicators over Two Centuries of the S&P 500 Index," Working Papers 201606, University of Pretoria, Department of Economics.
    14. Khalid Khan & Chi-Wei Su & Adnan Khurshid & Muhammad Umar, 2022. "Are there bubbles in the vanilla price?," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-16, December.
    15. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    16. Guilherme Demos & Didier Sornette, 2017. "Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions," Papers 1707.07162, arXiv.org.
    17. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    18. Mark Mizraki, 2015. "Conversation with Mark Mizruchi:“There is Very Little Organizational Theory Left in Sociology Departments”," Journal of Economic Sociology, National Research University Higher School of Economics, vol. 16(3), pages 14-25.
    19. Martin Herdegen & Sebastian Herrmann, 2017. "Strict Local Martingales and Optimal Investment in a Black-Scholes Model with a Bubble," Papers 1711.06679, arXiv.org.
    20. Shu, Min & Zhu, Wei, 2020. "Detection of Chinese stock market bubbles with LPPLS confidence indicator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    21. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    22. Yao, Can-Zhong & Li, Hong-Yu, 2021. "A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    23. C. Vladimir Rodríguez-Caballero & Mauricio Villanueva-Domínguez, 2022. "Predicting cryptocurrency crash dates," Empirical Economics, Springer, vol. 63(6), pages 2855-2873, December.
    24. Daniel T. Pele, 2012. "An Lppl Algorithm For Estimating The Critical Time Of A Stock Market Bubble," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 1(2), pages 14-22, DECEMBER.
    25. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2017. "On the Predictability of Stock Market Bubbles: Evidence from LPPLS ConfidenceTM Multi-scale Indicators," Working Papers 201752, University of Pretoria, Department of Economics.
    26. Christopher Lynch & Benjamin Mestel, 2017. "Logistic Model For Stock Market Bubbles And Anti-Bubbles," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(06), pages 1-24, September.
    27. Marco Bianchetti & Davide Galli & Camilla Ricci & Angelo Salvatori & Marco Scaringi, 2016. "Brexit or Bremain ? Evidence from bubble analysis," Papers 1606.06829, arXiv.org.
    28. Dean Fantazzini, 2011. "Forecasting the Global Financial Crisis in the Years 2009-2010: Ex-post Analysis," Economics Bulletin, AccessEcon, vol. 31(4), pages 3259-3267.
    29. Fry, John, 2012. "Exogenous and endogenous crashes as phase transitions in complex financial systems," MPRA Paper 36202, University Library of Munich, Germany.
    30. Hideyuki Takagi, 2021. "Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator," Papers 2110.06190, arXiv.org.
    31. John Fry & McMillan David, 2015. "Stochastic modelling for financial bubbles and policy," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1002152-100, December.
    32. Kwangwon Ahn & Hanwool Jang & Jinu Kim & Inug Ryu, 2024. "COVID-19 and REITs Crash: Predictability and Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1159-1172, March.
    33. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    34. Hardik Rajpal & Deepak Dhar, 2018. "Achieving Perfect Coordination amongst Agents in the Co-Action Minority Game," Games, MDPI, vol. 9(2), pages 1-13, May.
    35. Ji, Hongyun & Zhang, Han, 2024. "Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    36. José Parra-Moyano & Daniel Partida & Moritz Gessl & Somnath Mazumdar, 2024. "Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models," Digital Finance, Springer, vol. 6(3), pages 427-439, September.
    37. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 2021 Bitcoin Bubbles and Crashes—Detection and Classification," Stats, MDPI, vol. 4(4), pages 1-21, November.
    38. MITRACHE, Mihai-Andrei & BOITOUT, Nicolas, 2017. "Tracking Financial Bubbles On Romania Stock Market," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 21(1), pages 41-62.
    39. Pele, Daniel Traian & Mazurencu-Marinescu-Pele, Miruna, 2018. "Cryptocurrencies, Metcalfe's law and LPPL models," IRTG 1792 Discussion Papers 2018-056, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    40. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    41. Jang, Hanwool & Song, Yena & Ahn, Kwangwon, 2020. "Can government stabilize the housing market? The evidence from South Korea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    42. Zhang, Yue-Jun & Yao, Ting, 2016. "Interpreting the movement of oil prices: Driven by fundamentals or bubbles?," Economic Modelling, Elsevier, vol. 55(C), pages 226-240.
    43. Charalambos Pitros, 2014. "UK housing bubble case study analysis: The ‘‘behaviour’’ of UK housing bubbles and the ‘‘affordability’’ parameter," ERES eres2014_4, European Real Estate Society (ERES).
    44. Hanwool Jang & Yena Song & Sungbin Sohn & Kwangwon Ahn, 2018. "Real Estate Soars and Financial Crises: Recent Stories," Sustainability, MDPI, vol. 10(12), pages 1-12, December.
    45. Sandro Lera & Didier Sornette, 2015. "Secular bipolar growth rate of the real US GDP per capita: implications for understanding past and future economic growth," Papers 1607.04136, arXiv.org.

  20. Fantazzini, Dean & Hook, Mikael & Angelantoni, André, 2011. "Global oil risks in the early 21st century," MPRA Paper 33825, University Library of Munich, Germany.

    Cited by:

    1. Ringsmuth, Andrew K. & Landsberg, Michael J. & Hankamer, Ben, 2016. "Can photosynthesis enable a global transition from fossil fuels to solar fuels, to mitigate climate change and fuel-supply limitations?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 134-163.
    2. Walan, Petter & Davidsson, Simon & Johansson, Sheshti & Höök, Mikael, 2014. "Phosphate rock production and depletion: Regional disaggregated modeling and global implications," Resources, Conservation & Recycling, Elsevier, vol. 93(C), pages 178-187.
    3. Tang, Xu & Zhang, Baosheng & Feng, Lianyong & Snowden, Simon & Höök, Mikael, 2012. "Net oil exports embodied in China's international trade: An input–output analysis," Energy, Elsevier, vol. 48(1), pages 464-471.
    4. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    5. Cho, Seong-Hoon & Bowker, J.M. & English, Donald B.K. & Roberts, Roland K. & Kim, Taeyoung, 2014. "Effects of travel cost and participation in recreational activities on national forest visits," Forest Policy and Economics, Elsevier, vol. 40(C), pages 21-30.
    6. Wang, Jianliang & Feng, Lianyong & Tverberg, Gail E., 2013. "An analysis of China's coal supply and its impact on China's future economic growth," Energy Policy, Elsevier, vol. 57(C), pages 542-551.
    7. Brutschin, Elina & Fleig, Andreas, 2018. "Geopolitically induced investments in biofuels," Energy Economics, Elsevier, vol. 74(C), pages 721-732.
    8. Lutz, Christian & Lehr, Ulrike & Wiebe, Kirsten S., 2012. "Economic effects of peak oil," Energy Policy, Elsevier, vol. 48(C), pages 829-834.
    9. Misbah Saboohi, 2020. "Exploring the Compensation Plans Under International Laws from Offshore Oil Facilities and Relationship between Oil Production, Trade and Carbon Emission: An Evidence from Global Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 265-273.
    10. Robert J. Brecha, 2013. "Ten Reasons to Take Peak Oil Seriously," Sustainability, MDPI, vol. 5(2), pages 1-31, February.
    11. Donohue, Ian & Coscieme, Luca & Gellner, Gabriel & Yang, Qiang & Jackson, Andrew L. & Kubiszewski, Ida & Costanza, Robert & McCann, Kevin S., 2023. "Accelerated economic recovery in countries powered by renewables," Ecological Economics, Elsevier, vol. 212(C).
    12. Höök, Mikael & Fantazzini, Dean & Angelantoni, André & Snowden, Simon, 2013. "Hydrocarbon liquefaction: viability as a peak oil mitigation strategy," MPRA Paper 46957, University Library of Munich, Germany.
    13. Ali Mirchi & Saeed Hadian & Kaveh Madani & Omid M. Rouhani & Azadeh M. Rouhani, 2012. "World Energy Balance Outlook and OPEC Production Capacity: Implications for Global Oil Security," Energies, MDPI, vol. 5(8), pages 1-26, July.
    14. Dean Fantazzini & Mario Maggi, 2014. "Proposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?," DEM Working Papers Series 082, University of Pavia, Department of Economics and Management.
    15. Höök, Mikael & Tang, Xu, 2013. "Depletion of fossil fuels and anthropogenic climate change—A review," Energy Policy, Elsevier, vol. 52(C), pages 797-809.
    16. Chun-Che Huang & Wen-Yau Liang & Roger R. Gung & Pei-An Wang, 2023. "Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    17. Ali Raza & Maryam Khokhar & Reyna Esperanza Zea Gordillo & Faisal Ejaz & Tahir Saeed Jagirani & Fodor Zita Júlia & Md Billal Hossain, 2024. "Economic Gains and Losses for Sustainable Policy Development of Crude Oil Resources: A Historical Perspective of Indian Subcontinent," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 642-655, March.

  21. Carluccio Bianchi & Dean Fantazzini & Maria Elena De Giuli & Mario Maggi, 2009. "Small Sample Properties of Copula-GARCH Modelling: A Monte Carlo Study," Quaderni di Dipartimento 093, University of Pavia, Department of Economics and Quantitative Methods.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    3. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," MPRA Paper 95992, University Library of Munich, Germany.

  22. Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario A. Maggi, 2009. "A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting," Quaderni di Dipartimento 105, University of Pavia, Department of Economics and Quantitative Methods.

    Cited by:

    1. Luca, Giovanni De & Guégan, Dominique & Rivieccio, Giorgia, 2019. "Assessing tail risk for nonlinear dependence of MSCI sector indices: A copula three-stage approach," Finance Research Letters, Elsevier, vol. 30(C), pages 327-333.
    2. Liu, Xiaoliang & Xu, Wei & Odening, Martin, 2011. "Can crop yield risk be globally diversified?," SFB 649 Discussion Papers 2011-018, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Rivieccio, Giorgia & De Luca, Giovanni, 2016. "Copula function approaches for the analysis of serial and cross dependence in stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 55-61.
    4. Pérez-Rodríguez, Jorge V. & Ledesma-Rodríguez, Francisco & Santana-Gallego, María, 2015. "Testing dependence between GDP and tourism's growth rates," Tourism Management, Elsevier, vol. 48(C), pages 268-282.

Articles

  1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    See citations under working paper version above.
  2. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    See citations under working paper version above.
  3. Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
    See citations under working paper version above.
  4. Kolesnikova, Anna & Fantazzini, Dean, 2021. "Asymmetry and hysteresis in the Russian gasoline market: The rationale for green energy exports," Energy Policy, Elsevier, vol. 157(C).
    See citations under working paper version above.
  5. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    See citations under working paper version above.
  6. Dean Fantazzini & Nikita Kolodin, 2020. "Does the Hashrate Affect the Bitcoin Price?," JRFM, MDPI, vol. 13(11), pages 1-29, October.
    See citations under working paper version above.
  7. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    See citations under working paper version above.
  8. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    See citations under working paper version above.
  9. T. Bazhenov & D. Fantazzini, 2019. "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility," Russian Journal of Industrial Economics, MISIS, vol. 12(1).
    See citations under working paper version above.
  10. Fantazzini, Dean & Shakleina, Marina & Yuras, Natalia, 2018. "Big Data for computing social well-being indices of the Russian population," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 43-66.

    Cited by:

    1. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.

  11. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.

    Cited by:

    1. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    2. Nadler, Philip & Guo, Yike, 2020. "The fair value of a token: How do markets price cryptocurrencies?," Research in International Business and Finance, Elsevier, vol. 52(C).
    3. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    4. Fruehwirt, Wolfgang & Hochfilzer, Leonhard & Weydemann, Leonard & Roberts, Stephen, 2021. "Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis," Finance Research Letters, Elsevier, vol. 40(C).
    5. Zura Kakushadze & Jim Kyung-Soo Liew, 2018. "CryptoRuble: From Russia with Love," Papers 1801.05760, arXiv.org.

  12. Fantazzini, Dean, 2016. "The oil price crash in 2014/15: Was there a (negative) financial bubble?," Energy Policy, Elsevier, vol. 96(C), pages 383-396.
    See citations under working paper version above.
  13. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    See citations under working paper version above.
  14. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    See citations under working paper version above.
  15. Eduardo Rossi & Dean Fantazzini, 2015. "Long Memory and Periodicity in Intraday Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 922-961.
    See citations under working paper version above.
  16. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    See citations under working paper version above.
  17. Larsson, Simon & Fantazzini, Dean & Davidsson, Simon & Kullander, Sven & Höök, Mikael, 2014. "Reviewing electricity production cost assessments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 170-183.
    See citations under working paper version above.
  18. Dean Fantazzini & Nikita Fomichev, 2014. "Forecasting the real price of oil using online search data," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 4-31.

    Cited by:

    1. Fantazziini, Dean, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US using Online Search Data," MPRA Paper 59696, University Library of Munich, Germany.
    2. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    3. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    4. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    5. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German Car Sales Using Google Data and Multivariate Models," MPRA Paper 67110, University Library of Munich, Germany.
    6. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    7. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    8. Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
    9. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    10. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    11. Miao, Miao & Khaskheli, Asadullah & Raza, Syed Ali & Yousufi, Sara Qamar, 2022. "Using internet search keyword data for predictability of precious metals prices: Evidence from non-parametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 75(C).
    12. Mr. Serhan Cevik, 2020. "Where Should We Go? Internet Searches and Tourist Arrivals," IMF Working Papers 2020/022, International Monetary Fund.
    13. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
    14. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    15. Dean Fantazzini & Mario Maggi, 2014. "Proposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?," DEM Working Papers Series 082, University of Pavia, Department of Economics and Management.
    16. Fantazzini, Dean, 2014. "Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling'," MPRA Paper 55430, University Library of Munich, Germany.
    17. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.

  19. Petr Geraskin & Dean Fantazzini, 2013. "Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
    See citations under working paper version above.
  20. Frolova, Elvina & Fantazzini, Dean, 2012. "Credit default swaps and CDS-bond basis with Russian companies: a review and an analysis of the effects of the short selling ban during the second great contraction," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 25(1), pages 3-24.

    Cited by:

    1. Yan Yan & Zhewen Liao & Xiaosong Chen, 2018. "Fixed-income securities: bibliometric review with network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1615-1640, September.

  21. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.

    Cited by:

    1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 90-110.
    2. Travkin, Alexandr, 2013. "Pair copula constructions in portfolio optimization ploblem," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 32(4), pages 110-133.
    3. Kalyagin, V. & Koldanov, A. & Koldanov, P. & Pardalos, P., 2017. "Statistical Procedures for Stock Markets Network Structures Identification," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 33-52.
    4. Knyazev, Alexander & Lepekhin, Oleg & Shemyakin, Arkady, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    5. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    6. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 18-38.
    7. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 100-130.
    8. Travkin, A., 2015. "Estimating Pair-Copula Constructions Using Empirical Tail Dependence Functions: an Application to Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 25(1), pages 39-55.
    9. Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 113-130.

  22. Fantazzini, Dean & Höök, Mikael & Angelantoni, André, 2011. "Global oil risks in the early 21st century," Energy Policy, Elsevier, vol. 39(12), pages 7865-7873.
    See citations under working paper version above.
  23. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 100-130.

    Cited by:

    1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 90-110.
    2. Travkin, Alexandr, 2013. "Pair copula constructions in portfolio optimization ploblem," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 32(4), pages 110-133.
    3. Kalyagin, V. & Koldanov, A. & Koldanov, P. & Pardalos, P., 2017. "Statistical Procedures for Stock Markets Network Structures Identification," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 33-52.
    4. Knyazev, Alexander & Lepekhin, Oleg & Shemyakin, Arkady, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    5. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    6. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 18-38.
    7. Travkin, A., 2015. "Estimating Pair-Copula Constructions Using Empirical Tail Dependence Functions: an Application to Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 25(1), pages 39-55.
    8. Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 113-130.

  24. Carluccio Bianchi & Maria Elena De Giuli & Dean Fantazzini & Mario Maggi, 2011. "Small sample properties of copula-GARCH modelling: a Monte Carlo study," Applied Financial Economics, Taylor & Francis Journals, vol. 21(21), pages 1587-1597.
    See citations under working paper version above.
  25. Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario Maggi, 2010. "A copula-VAR-X approach for industrial production modelling and forecasting," Applied Economics, Taylor & Francis Journals, vol. 42(25), pages 3267-3277.
    See citations under working paper version above.
  26. Dean Fantazzini, 2010. "Modelling and forecasting the global financial crisis: Initial findings using heterosckedastic log-periodic models," Economics Bulletin, AccessEcon, vol. 30(3), pages 1833-1841.

    Cited by:

    1. Fantazzini, Dean & Geraskin, Petr, 2011. "Everything You Always Wanted to Know about Log Periodic Power Laws for Bubble Modelling but Were Afraid to Ask," MPRA Paper 47869, University Library of Munich, Germany.
    2. Dean Fantazzini, 2011. "Forecasting the Global Financial Crisis in the Years 2009-2010: Ex-post Analysis," Economics Bulletin, AccessEcon, vol. 31(4), pages 3259-3267.
    3. Phong Nguyen & Wei-han Liu, 2017. "Time-Varying Linkage of Possible Safe Haven Assets: A Cross-Market and Cross-asset Analysis," International Review of Finance, International Review of Finance Ltd., vol. 17(1), pages 43-76, March.

  27. Fantazzini, Dean, 2010. "Three-stage semi-parametric estimation of T-copulas: Asymptotics, finite-sample properties and computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2562-2579, November.

    Cited by:

    1. Fantazzini, Dean, 2020. "Discussing copulas with Sergey Aivazian: a memoir," MPRA Paper 102317, University Library of Munich, Germany.
    2. Paolella, Marc S. & Polak, Paweł, 2015. "ALRIGHT: Asymmetric LaRge-scale (I)GARCH with Hetero-Tails," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 282-297.
    3. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    4. Carluccio Bianchi & Dean Fantazzini & Maria Elena De Giuli & Mario Maggi, 2009. "Small Sample Properties of Copula-GARCH Modelling: A Monte Carlo Study," Quaderni di Dipartimento 093, University of Pavia, Department of Economics and Quantitative Methods.
    5. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    6. Jin Zhang & Wing Long Ng, 2010. "Exact Maximum Likelihood Estimation for Copula Models," Working Papers 038, COMISEF.
    7. Jin Zhang & Dietmar Maringer, 2010. "Asset Pair-Copula Selection with Downside Risk Minimization," Working Papers 037, COMISEF.

  28. Fantazzini, Dean, 2009. "The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2168-2188, April.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Matthias R. Fengler & Helmut Herwartz & Christian Werner, 2012. "A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew," Journal of Financial Econometrics, Oxford University Press, vol. 10(3), pages 457-493, June.
    3. Fantazzini , Dean, 2009. "Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 14(2), pages 100-127.
    4. Nathan Lael Joseph & Thi Thuy Anh Vo & Asma Mobarek & Sabur Mollah, 2020. "Volatility and asymmetric dependence in Central and East European stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1241-1303, November.
    5. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    6. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    7. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.
    8. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    9. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    10. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    11. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    12. Penikas, Henry, 2010. "Copula-Models in Foreign Exchange Risk-Management of a Bank," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 62-87.
    13. Sosheel S. Godfrey & Thomas Nordblom & Ryan H. L. Ip & Susan Robertson & Timothy Hutchings & Karl Behrendt, 2021. "Drought Shocks and Gearing Impacts on the Profitability of Sheep Farming," Agriculture, MDPI, vol. 11(4), pages 1-19, April.
    14. Mark Mizraki, 2015. "Conversation with Mark Mizruchi:“There is Very Little Organizational Theory Left in Sociology Departments”," Journal of Economic Sociology, National Research University Higher School of Economics, vol. 16(3), pages 14-25.
    15. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    16. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2012. "Global Risk Evolution and Diversification: a Copula-DCC-GARCH Model Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(4), pages 529-550.
    17. Fernanda Maria Müller & Thalles Weber Gössling & Samuel Solgon Santos & Marcelo Brutti Righi, 2024. "A comparison of Range Value at Risk (RVaR) forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 509-543, April.
    18. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    19. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    20. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    21. Noureddine Benlagha, 2014. "Dependence structure between nominal and index-linked bond returns: a bivariate copula and DCC-GARCH approach," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3849-3860, November.
    22. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "Long Memory and Tail dependence in Trading Volume and Volatility," CREATES Research Papers 2009-30, Department of Economics and Business Economics, Aarhus University.
    23. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    24. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
    25. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 11(3), pages 87-122.
    26. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.
    27. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.
    28. Ausin, M. Concepcion & Lopes, Hedibert F., 2010. "Time-varying joint distribution through copulas," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2383-2399, November.
    29. Berger, Theo, 2016. "On the isolated impact of copulas on risk measurement: Asimulation study," Economic Modelling, Elsevier, vol. 58(C), pages 475-481.

  29. Fantazzini , Dean, 2009. "Credit Risk Management (Cont.)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 105-138.

    Cited by:

    1. Bologov , Yaroslav, 2013. "A copula-based approach to portfolio credit risk modeling," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 29(1), pages 45-66.
    2. Казакова К.А. & Князев А.Г. & Лепёхин О.А., 2015. "Оптимальный размер банковского резерва: прогноз просроченной кредитной задолженности с использованием копулярных моделей. Optimum volume of bank reserve: forecasting of overdue credit indebtedness usi," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 15(4), pages 59-76.
    3. Брагин Антон Игоревич & Кузнецов Евгений Николаевич, 2011. "Анализ Значений Суверенного Кредитного Рейтинга И Его Моделирование," Российский внешнеэкономический вестник, CyberLeninka;Государственное образовательное учреждение Высшего профессионального образования Всероссийская академия внешней торговли Минэкономразвития России, vol. 2011(12), pages 21-36.

  30. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Kim, Dongwoo, 2024. "Corporate loan duration, macroeconomic environments, and COVID-19," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1088-1103.
    3. Sangcheol Song, 2014. "Entry mode irreversibility, host market uncertainty, and foreign subsidiary exits," Asia Pacific Journal of Management, Springer, vol. 31(2), pages 455-471, June.
    4. Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023. "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    5. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    6. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    7. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    8. Bing Xu & Jingwen Yang & Bifei Sun, 2018. "A nonparametric decision approach for entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 14(1), pages 5-14, March.
    9. Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2021. "Machine Learning and Credit Risk: Empirical Evidence from SMEs," DEM Working Papers Series 201, University of Pavia, Department of Economics and Management.
    10. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    11. Yubin Yang & Xuejian Chu & Ruiqi Pang & Feng Liu & Peifang Yang, 2021. "Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    12. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    13. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.
    14. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    15. Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
    16. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    17. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.
    18. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    19. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    20. Hao Liu & Shijin Chen, 2015. "Credit Risk Measurement Based on the Markov Chain," Business and Management Research, Business and Management Research, Sciedu Press, vol. 4(3), pages 32-42, September.
    21. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    22. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    23. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    24. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
    25. Yao-Zhi Xu & Jian-Lin Zhang & Ying Hua & Lin-Yue Wang, 2019. "Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    26. Anna Burova & Henry Penikas & Svetlana Popova, 2021. "Probability of Default Model to Estimate Ex Ante Credit Risk," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 49-72, September.
    27. Shumin Bai & Xiaofeng Ji & Bingyou Dai & Yongming Pu & Wenwen Qin, 2022. "An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    28. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    29. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.
    30. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    31. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    32. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    33. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    34. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    35. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
    36. Sangcheol Song, 2014. "Subsidiary Divestment: The Role of Multinational Flexibility," Management International Review, Springer, vol. 54(1), pages 47-70, February.

  31. Fantazzini, Dean & DeGiuli, Maria Elena & Figini, Silvia & Giudici, Paolo, 2009. "Enhanced credit default models for heterogeneous SME segments," Journal of Financial Transformation, Capco Institute, vol. 25, pages 31-39.

    Cited by:

    1. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    2. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    3. Candida Bussoli & Mariateresa Cuoccio & Claudio Giannotti, 2021. "Discriminant Analysis and Firms’ Bankruptcy: Evidence from European SMEs," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(12), pages 164-164, July.
    4. Dean Fantazzini & Mario Maggi, 2014. "Proposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?," DEM Working Papers Series 082, University of Pavia, Department of Economics and Management.

  32. Maria Giuli & Dean Fantazzini & Mario Maggi, 2008. "A New Approach for Firm Value and Default Probability Estimation beyond Merton Models," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 161-180, March.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    3. Luciana Dalla Valle & Maria Elena De Giuli & Claudia Tarantola & Claudio Manelli, 2014. "Default Probability Estimation via Pair Copula Constructions," Papers 1405.1309, arXiv.org, revised Aug 2015.
    4. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.
    5. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    6. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    7. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    8. Muhammad Suhail Rizwan & Muhammad Moinuddin & Barbara L’Huillier & Dawood Ashraf, 2018. "Does a one-size-fits-all approach to financial regulations alleviate default risk? The case of dual banking systems," Journal of Regulatory Economics, Springer, vol. 53(1), pages 37-74, February.
    9. De Giuli, Maria Elena & Maggi, Mario Alessandro & Paris, Francesco Maria, 2009. "Deposit guarantee evaluation and incentives analysis in a mutual guarantee system," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1058-1068, June.
    10. Fantazzini , Dean, 2009. "Credit Risk Management (Cont.)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 105-138.

  33. Fantazzini, Dean, 2008. "An Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 10(2), pages 91-137.

    Cited by:

    1. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    2. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    3. Брагин Антон Игоревич & Кузнецов Евгений Николаевич, 2011. "Анализ Значений Суверенного Кредитного Рейтинга И Его Моделирование," Российский внешнеэкономический вестник, CyberLeninka;Государственное образовательное учреждение Высшего профессионального образования Всероссийская академия внешней торговли Минэкономразвития России, vol. 2011(12), pages 21-36.

  34. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.

    Cited by:

    1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    3. Sangcheol Song, 2014. "Entry mode irreversibility, host market uncertainty, and foreign subsidiary exits," Asia Pacific Journal of Management, Springer, vol. 31(2), pages 455-471, June.
    4. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    5. Bologov , Yaroslav, 2013. "A copula-based approach to portfolio credit risk modeling," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 29(1), pages 45-66.
    6. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.
    7. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    8. Raffaella Calabrese, 2011. "Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEs," Working Papers 201134, Geary Institute, University College Dublin.
    9. Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
    10. Казакова К.А. & Князев А.Г. & Лепёхин О.А., 2015. "Оптимальный размер банковского резерва: прогноз просроченной кредитной задолженности с использованием копулярных моделей. Optimum volume of bank reserve: forecasting of overdue credit indebtedness usi," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 15(4), pages 59-76.
    11. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.
    12. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    13. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    14. Брагин Антон Игоревич & Кузнецов Евгений Николаевич, 2011. "Анализ Значений Суверенного Кредитного Рейтинга И Его Моделирование," Российский внешнеэкономический вестник, CyberLeninka;Государственное образовательное учреждение Высшего профессионального образования Всероссийская академия внешней торговли Минэкономразвития России, vol. 2011(12), pages 21-36.
    15. Sangcheol Song, 2014. "Subsidiary Divestment: The Role of Multinational Flexibility," Management International Review, Springer, vol. 54(1), pages 47-70, February.
    16. Fantazzini , Dean, 2009. "Credit Risk Management (Cont.)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 105-138.

  35. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 11(3), pages 87-122.

    Cited by:

    1. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    2. Брагин Антон Игоревич & Кузнецов Евгений Николаевич, 2011. "Анализ Значений Суверенного Кредитного Рейтинга И Его Моделирование," Российский внешнеэкономический вестник, CyberLeninka;Государственное образовательное учреждение Высшего профессионального образования Всероссийская академия внешней торговли Минэкономразвития России, vol. 2011(12), pages 21-36.

Chapters

  1. Dean Fantazzini, 2011. "Fractionally Integrated Models for Volatility: A Review," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration, chapter 5, pages 104-123, Palgrave Macmillan.

    Cited by:

    1. Saker Sabkha & Christian Peretti & Dorra Hmaied, 2019. "The Credit Default Swap market contagion during recent crises: international evidence," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 1-46, July.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.