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Juri Marcucci

Citations

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

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.

    Mentioned in:

    1. New indicators of perceived inflation in France based on media data
      by raphael.moncomble in Eco Notepad on 2022-12-26 14:31:41

Working papers

  1. Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021. "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers) 1321, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Audinga Baltrunaite & Mario Cannella & Sauro Mocetti & Giacomo Roma, "undated". "Board composition and performance of state-owned enterprises: Quasi-experimental evidence," Temi di discussione (Economic working papers) 1328, Bank of Italy, Economic Research and International Relations Area.
    2. Claudia Maurini & Alessandro Schiavone, 2021. "The catalytic role of IMF programs," Temi di discussione (Economic working papers) 1331, Bank of Italy, Economic Research and International Relations Area.
    3. Claudia Pacella, 2021. "Dating the euro area business cycle: an evaluation," Temi di discussione (Economic working papers) 1332, Bank of Italy, Economic Research and International Relations Area.
    4. Gerardin Mathilde, & Ranvier Martial., 2021. "Enrichment of the Banque de France’s monthly business survey: lessons from textual analysis of business leaders’ comments," Working papers 821, Banque de France.
    5. Valentina Michelangeli & Eliana Viviano, 2021. "Can internet banking affect households' participation in financial markets and financial awareness?," Temi di discussione (Economic working papers) 1329, Bank of Italy, Economic Research and International Relations Area.
    6. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    7. Jonathan Huntley & Valentina Michelangeli & Felix Reichling, 2021. "What drives investors to chase returns?," Temi di discussione (Economic working papers) 1334, Bank of Italy, Economic Research and International Relations Area.
    8. Lorenzo Bencivelli & Beniamino Pisicoli, 2021. "Foreign investors and target firms’ financial structure: cavalry or locusts?," Temi di discussione (Economic working papers) 1327, Bank of Italy, Economic Research and International Relations Area.
    9. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    10. Olivier De Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.

  2. Cristina Angelico & Juri Marcucci & Marcello Miccoli & Filippo Quarta, 2021. "Can we measure inflation expectations using Twitter?," Temi di discussione (Economic working papers) 1318, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021. "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers) 1321, Bank of Italy, Economic Research and International Relations Area.
    2. Mirko Djukic, 2024. "Topic classification of economic newspaper articles in a highly inflectional language – the case of Serbia," Working Papers Bulletin 21, National Bank of Serbia.
    3. Xinyu Li & Zihan Tang, 2022. "Sentiment Analysis on Inflation after Covid-19," Papers 2209.14737, arXiv.org, revised Dec 2022.
    4. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin J. Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," JRFM, MDPI, vol. 16(3), pages 1-28, March.
    5. Marc-André Gosselin & Temel Taskin, 2023. "What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?," Discussion Papers 2023-13, Bank of Canada.
    6. Travis Adams & Andrea Ajello & Diego Silva & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Papers 2305.16164, arXiv.org.
    7. Donato Masciandaro & Davide Romelli & Gaia Rubera, 2023. "Monetary policy and financial markets: evidence from Twitter traffic," Trinity Economics Papers TEP1023, Trinity College Dublin, Department of Economics.
    8. Jouchi Nakajima & Hiroaki Yamagata & Tatsushi Okuda & Shinnosuke Katsuki & Takeshi Shinohara, 2021. "Extracting Firms' Short-Term Inflation Expectations from the Economy Watchers Survey Using Text Analysis," Bank of Japan Working Paper Series 21-E-12, Bank of Japan.
    9. J. Daniel Aromí & Martín Llada, 2024. "Are professional forecasters inattentive to public discussions about inflation? The case of Argentina," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2572-2587, November.
    10. Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    11. Tetiana Yukhymenko, 2021. "Role of the Media in the Inflation Expectation Formation Process," IHEID Working Papers 13-2021, Economics Section, The Graduate Institute of International Studies.
    12. Swapnil Virendra Chalwadi & Preeti Tushar Joshi & Nitin Mohanlal Sharma & Chaitanya Gite & Sangita Salve, 2023. "Gender Differences in Inflation Expectations: Recent Evidence from India," Administrative Sciences, MDPI, vol. 13(2), pages 1-14, February.
    13. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," CIRANO Working Papers 2022s-27, CIRANO.
    14. Chee-Hong Law & Kim Huat Goh, 2024. "A systematic literature review of the implications of media on inflation expectations," International Economics and Economic Policy, Springer, vol. 21(2), pages 311-340, May.
    15. Vyshnevskyi, Iegor & Jombo, Wytone & Sohn, Wook, 2024. "The clarity of monetary policy communication and financial market volatility in developing economies," Emerging Markets Review, Elsevier, vol. 59(C).
    16. Vivian Chu & Tatjana Dahlhaus & Christopher Hajzler & Pierre-Yves Yanni, 2023. "Digitalization: Implications for Monetary Policy," Discussion Papers 2023-18, Bank of Canada.
    17. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    18. J. Daniel Aromí & Martín Llada, 2024. "Are professional forecasters inattentive to public discussions? The case of inflation in Argentina," Working Papers 300, Red Nacional de Investigadores en Economía (RedNIE).
    19. Andrea Ajello & Diego Silva & Travis Adams & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Finance and Economics Discussion Series 2023-034, Board of Governors of the Federal Reserve System (U.S.).
    20. Arndt, Sarah, 2024. "Different Newspapers – Different Inflation Perceptions," Working Papers 0748, University of Heidelberg, Department of Economics.
    21. Cafferata, Alessia & Cerruti, Gianluca & Mazzone, Giulio, 2022. "Taxation, health system endowment and quality of institutions: a "social" perception across Europe," MPRA Paper 112118, University Library of Munich, Germany.
    22. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers 1374, Board of Governors of the Federal Reserve System (U.S.).
    23. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    24. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    25. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    26. Petrova, Diana, 2022. "Assessment of inflation expectations based on internet data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 25-38.
    27. Xinyu Li & Zihan Tang, 2023. "Sentiment Analysis on Inflation after COVID-19," Applied Economics and Finance, Redfame publishing, vol. 10(1), pages 1023-1023, February.
    28. Borgioli, Stefano & Gallo, Giampiero M. & Ongari, Chiara, 2024. "Financial returns, sentiment and market volatility. A dynamic assessment," Working Paper Series 2999, European Central Bank.
    29. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    30. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    31. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
    32. Cafferata, Alessia & Cerruti, Gianluca & Mazzone, Giulio, 2023. "Taxation, health system endowment and institutional quality: ‘Social media’ perceptions across Europe," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 224-243.
    33. Rosalind L. Bennett & Manju Puri & Paul E. Soto, 2024. "Inside the Boardroom: Evidence from the Board Structure and Meeting Minutes of Community Banks," Finance and Economics Discussion Series 2024-085, Board of Governors of the Federal Reserve System (U.S.).
    34. Olivier De Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.
    35. Valerio Astuti & Marta Crispino & Marco Langiulli & Juri Marcucci, 2022. "Textual analysis of a Twitter corpus during the COVID-19 pandemics," Questioni di Economia e Finanza (Occasional Papers) 692, Bank of Italy, Economic Research and International Relations Area.
    36. Giulio Gariano & Gianluca Viggiano, 2022. "Press news and social media in credit risk assessment: the experience of Banca d’Italia’s In-house Credit Assessment System," Temi di discussione (Economic working papers) 24, Bank of Italy, Economic Research and International Relations Area.

  3. Guerino Ardizzi & Simone Emiliozzi & Juri Marcucci & Libero Monteforte, 2019. "News and consumer card payments," Temi di discussione (Economic working papers) 1233, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021. "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers) 1321, Bank of Italy, Economic Research and International Relations Area.
    2. Guerino Ardizzi & Andrea Nobili & Giorgia Rocco, 2020. "A game changer in payment habits: evidence from daily data during a pandemic," Questioni di Economia e Finanza (Occasional Papers) 591, Bank of Italy, Economic Research and International Relations Area.

  4. Juri Marcucci & Paolo Emilio Mistrulli, 2013. "Female entrepreneurs in trouble: do their bad loans last longer?," Questioni di Economia e Finanza (Occasional Papers) 185, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Emilia Bonaccorsi di Patti & Cristina Demma & Davide Dottori & Giacinto Micucci, 2019. "Bad loan closure times in Italy," Questioni di Economia e Finanza (Occasional Papers) 532, Bank of Italy, Economic Research and International Relations Area.

  5. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Caetano, Marco Antonio Leonel, 2021. "Political activity in social media induces forest fires in the Brazilian Amazon," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    2. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    3. Monge, Manuel & Poza, Carlos & Borgia, Sofía, 2022. "A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues," International Economics, Elsevier, vol. 169(C), pages 1-12.
    4. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    5. Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
    6. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    7. Falik Shear & Badar Nadeem Ashraf & Mohsin Sadaqat, 2020. "Are Investors’ Attention and Uncertainty Aversion the Risk Factors for Stock Markets? International Evidence from the COVID-19 Crisis," Risks, MDPI, vol. 9(1), pages 1-15, December.
    8. Per Nymand-Andersen, 2016. "Big data: the hunt for timely insights and decision certainty," IFC Working Papers 14, Bank for International Settlements.
    9. Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
    10. Bentzen, Jeanet, 2020. "In Crisis, We Pray: Religiosity and the COVID-19 Pandemic," CEPR Discussion Papers 14824, C.E.P.R. Discussion Papers.
    11. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    12. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
    13. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    14. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    15. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    16. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Gonzalez-Fernandez, Marcos & Miffre, Joelle, 2020. "Fear of hazards in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 119(C).
    17. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    18. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Post-Print hal-03205161, HAL.
    19. Zhongchen Song & Tom Coupé, 2022. "Predicting Chinese consumption series with Baidu," Working Papers in Economics 22/19, University of Canterbury, Department of Economics and Finance.
    20. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    21. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    22. Michele Costola & Matteo Iacopini & Carlo R. M. A. Santagiustina, 2020. "Public Concern and the Financial Markets during the COVID-19 outbreak," Papers 2005.06796, arXiv.org.
    23. Bae, Siye & Jo, Soojin & Shim, Myungkyu, 2023. "United States of Mind under Uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 102-127.
    24. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    25. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    26. 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.
    27. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    28. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    29. Marcos González-Fernández & Carmen González-Velasco, 2019. "An approach to predict Spanish mortgage market activity using Google data," Economics and Business Letters, Oviedo University Press, vol. 8(4), pages 209-214.
    30. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving: Evidence from SMS Donations," CAGE Online Working Paper Series 536, Competitive Advantage in the Global Economy (CAGE).
    31. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
    32. Christian Conrad & Anessa Custovic & Eric Ghysels, 2018. "Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis," JRFM, MDPI, vol. 11(2), pages 1-12, May.
    33. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    34. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    35. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    36. Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.
    37. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    38. Federico Cingano & Marco Tonello, 2020. "Law Enforcement, Social Control and Organized Crime: Evidence from Local Government Dismissals in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(2), pages 221-254, July.
    39. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    40. Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
    41. 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).
    42. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    43. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    44. Costanza Catalano & Andrea Carboni & Claudio Doria, 2023. "How can Big Data improve the quality of tourism statistics? The Bank of Italy's experience in compiling the "travel" item in the Balance of Payments," Questioni di Economia e Finanza (Occasional Papers) 761, Bank of Italy, Economic Research and International Relations Area.
    45. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    46. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    47. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    48. Omid Zamani & Thomas Bittmann & Jens‐Peter Loy, 2024. "Does the internet bring food prices closer together? Exploring search engine query data in Iran," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 688-715, June.
    49. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    50. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    51. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    52. Bleher, Johannes & Dimpfl, Thomas, 2022. "Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption," Econometrics and Statistics, Elsevier, vol. 24(C), pages 1-26.
    53. Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
    54. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    55. Castelnuovo, Efrem & Duc Tran, Trung, 2017. "Google It Up! A Google Trends-based Uncertainty Index for the United States and Australia," MPRA Paper 82297, University Library of Munich, Germany.
    56. Matteo Iacopini & Carlo Romano Marcello Alessandro Santagiustina, 2021. "Filtering the Intensity of Public Concern from Social Media Count Data with Jumps," SciencePo Working papers Main hal-04494229, HAL.
    57. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
    58. Puhr, Harald & Müllner, Jakob, 2024. "Vox populi, vox dei: A concept and measure for grassroots socio-political risk using Google Trends," Journal of International Management, Elsevier, vol. 30(2).
    59. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    60. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
    61. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    62. Dimitrios Anastasiou & Zacharias Bragoudakis & Stelios Giannoulakis, 2020. "Perceived vs actual financial crisis and bank credit standards: is there any indication of self-fulfilling prophecy?," Working Papers 277, Bank of Greece.
    63. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    64. Pijush Kanti Das & Prabir Kumar Das, 2024. "Improvement in Inflation Forecasting: Ensembling Text Mining with Macro Data in Machine Learning Models," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 16(6), pages 1-92, June.
    65. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
    66. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    67. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    68. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.
    69. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    70. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    71. Ferrara, Laurent & Sheng, Xuguang Simon, 2022. "Guest editorial: Economic forecasting in times of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 527-528.
    72. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," CIRANO Working Papers 2022s-27, CIRANO.
    73. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    74. Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2019. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Papers 1901.11355, arXiv.org, revised Feb 2020.
    75. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    76. Reuben Ellul, 2018. "Forecasting unemployment rates in Malta: A labour market flows approach," CBM Working Papers WP/03/2018, Central Bank of Malta.
    77. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    78. Bertoni, Marco & Corazzini, Luca & Robone, Silvana, 2019. "Promoting Breast Cancer Screening Take-Ups with Zero Cost: Evidence from an Experiment on Formatting Invitation Letters in Italy," IZA Discussion Papers 12193, Institute of Labor Economics (IZA).
    79. 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).
    80. Fabrizio Ferriani & Andrea Gazzani, 2021. "Financial condition indices for emerging market economies: can Google help?," Questioni di Economia e Finanza (Occasional Papers) 653, Bank of Italy, Economic Research and International Relations Area.
    81. 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).
    82. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    83. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance 2020-04, Joint Research Centre, European Commission.
    84. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    85. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    86. Andrea Fasulo & Alessia Naccarato & Alessio Pizzichini, 2019. "Nowcasting the Italian unemployment rate with Google Trends," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 73(4), pages 29-40, October-D.
    87. 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.
    88. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    89. Jung, Alexander & Kühl, Patrick, 2021. "Can central bank communication help to stabilise inflation expectations?," Working Paper Series 2547, European Central Bank.
    90. Ahundjanov, Behzod B. & Akhundjanov, Sherzod B. & Okhunjanov, Botir B., 2021. "Risk perception and oil and gasoline markets under COVID-19," Journal of Economics and Business, Elsevier, vol. 115(C).
    91. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
    92. Behera, Sarthak & Sadana, Divya, 2022. "The Impact of Visibility on School Athletic Finances: An Empirical Analysis using Google Trends," MPRA Paper 114818, University Library of Munich, Germany.
    93. Edoardo Rainone, 2021. "Identifying deposits' outflows in real-time," Temi di discussione (Economic working papers) 1319, Bank of Italy, Economic Research and International Relations Area.
    94. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
    95. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    96. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).
    97. Mikhaylov, Dmitry, 2023. "Macroeconomic Forecasting with the Use of News Data," Working Papers w20220250, Russian Presidential Academy of National Economy and Public Administration.
    98. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    99. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    100. 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.
    101. Borgioli, Stefano & Gallo, Giampiero M. & Ongari, Chiara, 2024. "Financial returns, sentiment and market volatility. A dynamic assessment," Working Paper Series 2999, European Central Bank.
    102. Carlo Perroni & Kimberley Ann Scharf & Oleksandr Talavera & Linh Vi, 2021. "Does Online Salience Predict Charitable Giving? Evidence from SMS Text Donations," CESifo Working Paper Series 9436, CESifo.
    103. 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).
    104. Afees A. Salisu & Ahamuefula E. Ogbonna & Idris Adediran, 2021. "Stock‐induced Google trends and the predictability of sectoral stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 327-345, March.
    105. 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.
    106. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    107. William D. Larson & Tara M. Sinclair, 2020. "Nowcasting unemployment insurance claims in the time of COVID-19," CAMA Working Papers 2020-63, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    108. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    109. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    110. Jan Goebel & Christian Krekel & Tim Tiefenbach & Nicholas R. Ziebarth, 2014. "Natural Disaster, Environmental Concerns, Well-Being and Policy Action," CINCH Working Paper Series 1405, Universitaet Duisburg-Essen, Competent in Competition and Health.
    111. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    112. Simran, & Sharma, Anil Kumar, 2023. "Asymmetric impact of economic policy uncertainty on cryptocurrency market: Evidence from NARDL approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    113. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
    114. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    115. Chiara Sotis, 2021. "How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2001-2016, December.
    116. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
    117. Nicolás Gonzálvez‐Gallego & María Concepción Pérez‐Cárceles & Laura Nieto‐Torrejón, 2024. "Do search queries predict violence against women? A forecasting model based on Google Trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1607-1614, August.
    118. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia & Brzeszczyński, Janusz, 2024. "Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    119. Eric Bax, 2019. "Computing a Data Dividend," Papers 1905.01805, arXiv.org, revised Jun 2019.
    120. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    121. Ilias Georgakopoulos, 2019. "Income and wealth inequality in Malta: evidence from micro data," CBM Working Papers WP/03/2019, Central Bank of Malta.
    122. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    123. Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
    124. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
    125. Brown, Alasdair & Reade, J. James & Vaughan Williams, Leighton, 2019. "When are prediction market prices most informative?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 420-428.
    126. García, Juan R. & Pacce, Matías & Rodrigo, Tomasa & Ruiz de Aguirre, Pep & Ulloa, Camilo A., 2021. "Measuring and forecasting retail trade in real time using card transactional data," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1235-1246.

  6. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. R Naraidoo & I Paya, 2010. "Forecasting Monetary Policy Rules in South Africa," Working Papers 611194, Lancaster University Management School, Economics Department.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Mario Porqueddu & Fabrizio Venditti, 2012. "Do food commodity prices have asymmetric effects on Euro-Area inflation?," Temi di discussione (Economic working papers) 878, Bank of Italy, Economic Research and International Relations Area.
    4. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
    5. Francesco D’Amuri & Juri Marcucci, 2010. "“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index," Working Papers 2010.31, Fondazione Eni Enrico Mattei.
    6. Moramarco, Graziano, 2024. "Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States," International Journal of Forecasting, Elsevier, vol. 40(2), pages 777-795.
    7. Xu, Yongan & Li, Ming & Yan, Wen & Bai, Jiancheng, 2022. "Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty," Resources Policy, Elsevier, vol. 79(C).
    8. Caruso, Alberto, 2018. "Nowcasting with the help of foreign indicators: The case of Mexico," Economic Modelling, Elsevier, vol. 69(C), pages 160-168.
    9. Murat Midiliç, 2020. "Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 87-117, January.
    10. Ruthira Naraidoo & Ivan Paya, 2010. "Forecasting Monetary Rules in South Africa," Working Papers 201007, University of Pretoria, Department of Economics.
    11. Guillen, Osmani Teixeira Carvalho & Hecq, Alain & Issler, João Victor & Saraiva, Diogo Vinícius Menezes, 2013. "Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 742, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    12. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. Pincheira, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2017. "Forecasting Inflation in Latin America with Core Measures," MPRA Paper 80496, University Library of Munich, Germany.
    14. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    15. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    16. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
    17. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    18. Pablo Pincheira Brown & Nicolás Hardy, 2024. "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1835-1858, September.
    19. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    20. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    21. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    22. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    23. Fuwei Jiang & Joshua Lee & Xiumin Martin & Guofu Zhou, 2019. "Manager sentiment and stock returns," CEMA Working Papers 677, China Economics and Management Academy, Central University of Finance and Economics.
    24. Neri, Marcelo Côrtes, 2014. "Brazil's middle classes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 759, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    25. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    26. Claire Giordano & Marco Marinucci & Andrea Silvestrini, 2022. "Assessing the usefulness of survey‐based data in forecasting firms' capital formation: Evidence from Italy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 491-513, April.
    27. Claire Giordano & Marco Marinucci & Andrea Silvestrini, 2021. "Forecasting corporate capital accumulation in Italy: the role of survey-based information," Questioni di Economia e Finanza (Occasional Papers) 596, Bank of Italy, Economic Research and International Relations Area.
    28. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    29. Chue, Timothy K. & Xu, Jin Karen, 2022. "Profitability, asset investment, and aggregate stock returns," Journal of Banking & Finance, Elsevier, vol. 143(C).
    30. Fabio Boschetti & Elizabeth A. Fulton & Nicola J. Grigg, 2014. "Citizens’ Views of Australia’s Future to 2050," Sustainability, MDPI, vol. 7(1), pages 1-26, December.
    31. E Pavlidis & I Paya & D Peel, 2009. "Forecasting the Real Exchange Rate using a Long Span of Data. A Rematch: Linear vs Nonlinear," Working Papers 601190, Lancaster University Management School, Economics Department.
    32. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    33. Li Guo & Lin Peng & Yubo Tao & Jun Tu, 2017. "Joint News, Attention Spillover,and Market Returns," Papers 1703.02715, arXiv.org, revised Nov 2022.
    34. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
    35. Pablo Pincheira & Nicolás Hardy & Felipe Muñoz, 2021. "“Go Wild for a While!”: A New Test for Forecast Evaluation in Nested Models," Mathematics, MDPI, vol. 9(18), pages 1-28, September.
    36. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    37. Pincheira, Pablo & Hardy, Nicolás & Muñoz, Felipe, 2021. ""Go wild for a while!": A new asymptotically Normal test for forecast evaluation in nested models," MPRA Paper 105368, University Library of Munich, Germany.
    38. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).

  7. D'Amuri, Francesco & Marcucci, Juri, 2009. "‘Google it!’ Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.

    Cited by:

    1. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    2. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 65-87.
    3. Monokroussos, George, 2015. "Nowcasting in Real Time Using Popularity Priors," MPRA Paper 68594, University Library of Munich, Germany.
    4. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    5. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    6. Pan, Wei-Fong, 2019. "Building sectoral job search indices for the United States," Economics Letters, Elsevier, vol. 180(C), pages 89-93.
    7. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
    8. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
    9. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
    10. Maria De Paola & Vincenzo Scoppa, 2013. "Consumers’ Reactions to Negative Information on Product Quality: Evidence from Scanner Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 42(3), pages 235-280, May.
    11. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
    12. Cedric Mbanga & Ali F. Darrat & Jung Chul Park, 2019. "Investor sentiment and aggregate stock returns: the role of investor attention," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 397-428, August.
    13. Konstantin A. Kholodilin & Boriss Siliverstovs, 2010. "Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany," Discussion Papers of DIW Berlin 1036, DIW Berlin, German Institute for Economic Research.
    14. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    15. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
    16. Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higad & Fabrice Murtin & Claudia Senik, 2015. "Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the US," SciencePo Working papers Main hal-03429943, HAL.
    17. Jorge M. Agüero & Trinidad Beleche, 2016. "Health Shocks and the Long-Lasting Change in Health Behaviors: Evidence from Mexico," Working papers 2016-26, University of Connecticut, Department of Economics.
    18. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
    19. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    20. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
    21. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
    22. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
    23. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    24. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    25. Bai, Lijuan & Yan, Xiangbin & Yu, Guang, 2019. "Impact of CEO media appearance on corporate performance in social media," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    26. 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.
    27. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2013. "Searching for Physical and Digital Media: The Evolution of Platforms for Finding Books," Working Papers 2013-04, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    28. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
    29. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    30. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    31. Algan, Yann & Beasley, Elizabeth & Guyot, Florian & Higa, Kazuhito & Murtin, Fabrice & Senik, Claudia, 2016. "Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the United States," CEPREMAP Working Papers (Docweb) 1605, CEPREMAP.
    32. Agüero, Jorge M. & Beleche, Trinidad, 2017. "Health shocks and their long-lasting impact on health behaviors: Evidence from the 2009 H1N1 pandemic in Mexico," Journal of Health Economics, Elsevier, vol. 54(C), pages 40-55.
    33. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
    34. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
    35. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    36. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
    37. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    38. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    39. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    40. Luigi Curini & Stefano Iacus & Luciano Canova, 2015. "Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(2), pages 525-542, April.
    41. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    42. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    43. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    44. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    45. Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.
    46. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.
    47. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    48. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
    49. Pietro Giorgio Lovaglio, 2022. "Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy," LABOUR, CEIS, vol. 36(1), pages 71-93, March.
    50. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute of Labor Economics (IZA).
    51. Azusa Matsumoto & Kohei Matsumura & Noriyuki Shiraki, 2013. "Potential of Search Data in Assessment of Current Economic Conditions," Bank of Japan Research Papers 2013-04-18, Bank of Japan.
    52. Florian Schaffner, 2015. "Predicting US bank failures with internet search volume data," ECON - Working Papers 214, Department of Economics - University of Zurich.

  8. Juri Marcucci & Mario Quagliariello, 2008. "Credit risk and business cycle over different regimes," Temi di discussione (Economic working papers) 670, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Grigori Fainstein & Igor Novikov, 2011. "The role of macroeconomic determinants in credit risk measurement in transition country: Estonian example," International Journal of Transitions and Innovation Systems, Inderscience Enterprises Ltd, vol. 1(2), pages 117-137.
    2. Uquillas, Adriana & Tonato, Ronny, 2022. "Inter-portfolio credit risk contagion including macroeconomic and financial factors: A case study for Ecuador," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 299-320.
    3. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    4. Apergis, Nicholas & Eleftheriou, Sofia, 2016. "Gold returns: Do business cycle asymmetries matter? Evidence from an international country sample," Economic Modelling, Elsevier, vol. 57(C), pages 164-170.
    5. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
    6. Mihail Petkovski & Jordan Kjosevski & Kiril Jovanovski, 2018. "Empirical Panel Analysis of Non-performing Loans in the Czech Republic. What are their Determinants and How Strong is their Impact on the Real Economy?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 68(5), pages 460-490, October.
    7. Anastasiou, Dimitrios, 2017. "Is ex-post credit risk affected by the cycles? The case of Italian banks," Research in International Business and Finance, Elsevier, vol. 42(C), pages 242-248.
    8. Anastasiou, Dimitrios, 2017. "The Interplay between Ex-post Credit Risk and the Cycles: Evidence from the Italian banks," MPRA Paper 79470, University Library of Munich, Germany.

  9. Francesca Lotti & Juri Marcucci, 2006. "Revisiting the empirical evidence on firms� money demand," Temi di discussione (Economic working papers) 595, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Sauro Mocetti, 2012. "Educational choices and the selection process: before and after compulsory schooling," Education Economics, Taylor & Francis Journals, vol. 20(2), pages 189-209, February.
    2. P Ganugi & L Grossi & G Ianulardo, 2009. "Scale Economies and Heterogeneity in Business Money Demand: The Italian Experience," Department of Economics Working Papers 17/09, University of Bath, Department of Economics.

  10. Juri Marcucci & Mario Quagliariello, "undated". "Is Bank Portfolio Riskiness Procyclical? Evidence from Italy using a Vector Autoregression," Discussion Papers 05/09, Department of Economics, University of York.

    Cited by:

    1. Del Gaudio, Belinda L. & Megaravalli, Amith V. & Sampagnaro, Gabriele & Verdoliva, Vincenzo, 2020. "Mandatory disclosure tone and bank risk-taking: Evidence from Europe," Economics Letters, Elsevier, vol. 186(C).
    2. Antonio Salvi & Candida Bussoli & Lavinia Conca & Marisa Gigante, 2021. "Determinants of Non-Performing Loans: Evidence from Europe," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(10), pages 230-230, July.
    3. Antonella Foglia, 2008. "Stress testing credit risk: a survey of authorities' approaches," Questioni di Economia e Finanza (Occasional Papers) 37, Bank of Italy, Economic Research and International Relations Area.
    4. Tajik, Mohammad & Aliakbari, Saeideh & Ghalia, Thaana & Kaffash, Sepideh, 2015. "House prices and credit risk: Evidence from the United States," Economic Modelling, Elsevier, vol. 51(C), pages 123-135.
    5. Stefano Puddu, 2013. "Real Sector and Banking System: Real and Feedback Effects. A Non-Linear VAR Approach," IRENE Working Papers 13-01, IRENE Institute of Economic Research.
    6. Alessandra Canepa & Fawaz Khaled, 2018. "Housing, Housing Finance and Credit Risk," IJFS, MDPI, vol. 6(2), pages 1-23, May.
    7. Rui Pascoal, 2012. "Macroeconomic Factors of Household Default. Is There Myopic Behaviour?," GEMF Working Papers 2012-20, GEMF, Faculty of Economics, University of Coimbra.
    8. Dua, Pami & Kapur, Hema, 2018. "Macro stress testing and resilience assessment of Indian banking," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 452-475.
    9. Baselga-Pascual, Laura & Trujillo-Ponce, Antonio & Cardone-Riportella, Clara, 2015. "Factors influencing bank risk in Europe: Evidence from the financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 138-166.
    10. Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.
    11. Inessa Love & Ms. Rima A Turk, 2013. "Macro-Financial Linkages in Egypt: A Panel Analysis of Economic Shocks and Loan Portfolio Quality," IMF Working Papers 2013/271, International Monetary Fund.
    12. Avignone, Giuseppe & Altunbas, Yener & Polizzi, Salvatore & Reghezza, Alessio, 2021. "Centralised or decentralised banking supervision? Evidence from European banks," Journal of International Money and Finance, Elsevier, vol. 110(C).
    13. Sreejata Banerjee & Divya Murali, 2015. "Stress Test of Banks in India: A VAR Approach," Working Papers 2015-102, Madras School of Economics,Chennai,India.
    14. Niyogi Sinha Roy, Tanima & Bhattacharya, Basabi, 2011. "Macroeconomic Stress Testing and the Resilience of the Indian Banking System: A Focus on Credit Risk," MPRA Paper 30263, University Library of Munich, Germany.
    15. Caporale, Guglielmo Maria & Di Colli, Stefano & Lopez, Juan Sergio, 2014. "Bank lending procyclicality and credit quality during financial crises," Economic Modelling, Elsevier, vol. 43(C), pages 142-157.
    16. Aykut Ekinci, 2016. "Rethinking Credit Risk under the Malinvestment Concept: The Case of Germany, Spain and Italy," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2016(1), pages 39-63.
    17. Mr. Reinout De Bock & Mr. Alexander Demyanets, 2012. "Bank Asset Quality in Emerging Markets: Determinants and Spillovers," IMF Working Papers 2012/071, International Monetary Fund.
    18. Mr. Ken Miyajima, 2016. "An Empirical Investigation of Oil-Macro-Financial Linkages in Saudi Arabia," IMF Working Papers 2016/022, International Monetary Fund.
    19. Skufi, Lorena, 2020. "Financial sector and macroeconomics links in MEAM," MPRA Paper 120481, University Library of Munich, Germany, revised 2020.
    20. Saadaoui Zied, 2015. "The Cyclical Behaviour of Bank Capital Buffers: An Empirical Evidence for MENA Banking Systems," Review of Middle East Economics and Finance, De Gruyter, vol. 11(2), pages 145-182, August.
    21. Kellen Kiambati, 2020. "Influence of credit risk on shareholder market value of commercial banks listed in Nairobi Securities Exchange," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 9(2), pages 107-117, March.
    22. Saleh Alodayni, 2016. "Oil Prices, Credit Risks in Banking Systems, and Macro-Financial Linkages across GCC Oil Exporters," IJFS, MDPI, vol. 4(4), pages 1-14, November.
    23. Zedginidze Zviad, 2012. "Linking Macroeconomic Dynamics to Georgian Credit Portfolio Risk," EERC Working Paper Series 12/07e, EERC Research Network, Russia and CIS.
    24. Gutierrez Girault, Matias Alfredo, 2008. "Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System," MPRA Paper 16378, University Library of Munich, Germany.
    25. Melecky, Ales & Melecky, Martin & Sulganova, Monika, 2014. "Úvěry v selhání a makroekonomika: Modelování systémového kreditního rizika v České republice [Non-performing loans and the macroeconomy: Modeling the systemic credit risk in Czech Republic]," MPRA Paper 59917, University Library of Munich, Germany.
    26. Renato Filosa, 2007. "Stress testing of the stability of the Italian banking system: a VAR approach," Heterogeneity and monetary policy 0703, Universita di Modena e Reggio Emilia, Dipartimento di Economia Politica.
    27. Sebastiano Laviola & Juri Marcucci & Mario Quagliariello, 2006. "Stress testing credit risk: experience from the italian FSAP," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 59(238), pages 269-291.
    28. Simona Castellani & Chiara Pederzoli & Costanza Torricelli, 2008. "Indebtedness, macroeconomic conditions and banks’ loan losses: evidence from Italy," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0009, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    29. Chiara Pederzoli & Costanza Torricelli & Simona Castellani, 2010. "The Interaction of Financial Fragility and the Business Cycle in Determining Banks’ Loan Losses: An Investigation of the Italian Case," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(3), pages 129-146, November.
    30. Gila-Gourgoura, E. & Nikolaidou, E., 2017. "Credit Risk Determinants in the Vulnerable Economies of Europe: Evidence from the Spanish Banking System," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 10(1), pages 60-71, March.
    31. Ruja, Catalin, 2014. "Macro Stress-Testing Credit Risk in Romanian Banking System," MPRA Paper 58244, University Library of Munich, Germany.
    32. Ahmed Bouteska & Mehdi Mili, 2022. "Women’s leadership impact on risks and financial performance in banking: evidence from the Southeast Asian Countries," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 26(4), pages 1213-1244, December.
    33. Martin Macháček & Aleš Melecký & Monika Šulganová, 2018. "Macroeconomic Drivers of Non-Performing Loans: A Meta-Regression Analysis," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(3), pages 351-374.
    34. Stefano Puddu, 2013. "Optimal Weights and Stress Banking Indexes," IRENE Working Papers 13-02, IRENE Institute of Economic Research.
    35. Hans Degryse & Sanja Jakovljević & Steven Ongena, 2015. "A Review of Empirical Research on the Design and Impact of Regulation in the Banking Sector," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 423-443, December.
    36. Coffinet, Jérôme & Coudert, Virginie & Pop, Adrian & Pouvelle, Cyril, 2012. "Two-way interplays between capital buffers and credit growth: Evidence from French banks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1110-1125.
    37. Antonella Foglia, 2009. "Stress Testing Credit Risk: A Survey of Authorities' Aproaches," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 9-45, September.
    38. Athanasoglou, Panayiotis P. & Daniilidis, Ioannis & Delis, Manthos D., 2014. "Bank procyclicality and output: Issues and policies," Journal of Economics and Business, Elsevier, vol. 72(C), pages 58-83.
    39. Marcello Pagnini & Paola Rossi & Valerio Vacca & Vincenzo Chiorazzo & Vincenzo D'Apice & Pierluigi Morelli & Giovanni Walter Puopolo, 2017. "Economic Activity and Credit Market Linkages: New Evidence From Italy," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 46(3), pages 491-526, November.
    40. Baltas, Konstantinos N. & Kapetanios, George & Tsionas, Efthymios & Izzeldin, Marwan, 2017. "Liquidity creation through efficient M&As: A viable solution for vulnerable banking systems? Evidence from a stress test under a panel VAR methodology," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 36-56.
    41. Liu, Guanchun & He, Lei & Yue, Yiding & Wang, Jiying, 2014. "The linkage between insurance activity and banking credit: Some evidence from dynamic analysis," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 239-265.
    42. Panayiotis P. Athanasoglou & Ioannis Daniilidis, 2011. "Procyclicality in the banking industry: causes, consequences and response," Working Papers 139, Bank of Greece.
    43. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
    44. Morone, Marco & Cornaglia, Anna, 2010. "An econometric model to quantify benchmark downturn LGD on residential mortgages," MPRA Paper 25588, University Library of Munich, Germany.
    45. Vasiliki Makri, 2016. "Towards an Investigation of Credit Risk Determinants in Eurozone Countries," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 15(1), pages 27-57, March.
    46. Vasiliki Makri, 2015. "What Triggers Loan Losses? An Empirical Investigation of Greek Financial Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 65(3-4), pages 119-143, july-Dece.
    47. Rasmus Kattai, 2010. "Credit risk model for the Estonian banking sector," Bank of Estonia Working Papers wp2010-01, Bank of Estonia, revised 04 Feb 2010.
    48. Abildgren, Kim, 2014. "Far out in the tails – The historical distributions of macro-financial risk factors in Denmark," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2014(1), pages 1-31.
    49. Gregoriou, Greg N. & Racicot, François-Éric & Théoret, Raymond, 2021. "The response of hedge fund tail risk to macroeconomic shocks: A nonlinear VAR approach," Economic Modelling, Elsevier, vol. 94(C), pages 843-872.
    50. Calmès, Christian & Théoret, Raymond, 2020. "Bank fee-based shocks and the U.S. business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    51. International Monetary Fund, 2010. "Colombia: Selected Issues Paper," IMF Staff Country Reports 2010/106, International Monetary Fund.
    52. Pami Dua & Hema Kapur, 2017. "Macro Stress Testing of Indian Bank Groups," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 11(4), pages 375-403, November.
    53. Laivi Laidroo, 2014. "Lending Growth and Cyclicality in Central and Eastern European Banks," TUT Economic Research Series 13, Department of Finance and Economics, Tallinn University of Technology.
    54. Ms. Mwanza Nkusu, 2011. "Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies," IMF Working Papers 2011/161, International Monetary Fund.
    55. Vasiliki Makri & Konstantinos Papadatos, 2014. "How accounting information and macroeconomic environment determine credit risk? Evidence from Greece," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 7(1), pages 129-143, April.
    56. Baselga-Pascual, Laura & Vähämaa, Emilia, 2021. "Female leadership and bank performance in Latin America," Emerging Markets Review, Elsevier, vol. 48(C).
    57. Abildgren, Kim, 2012. "Business cycles, monetary transmission and shocks to financial stability: empirical evidence from a new set of Danish quarterly national accounts 1948-2010," Working Paper Series 1458, European Central Bank.
    58. Ana Kundid Novokmet, 2015. "Cyclicality of bank capital buffers in South-Eastern Europe: endogenous and exogenous aspects," Financial Theory and Practice, Institute of Public Finance, vol. 39(2), pages 139-169.
    59. Laura Baselga-Pascual & Olga Del Orden-Olasagasti & Antonio Trujillo-Ponce, 2018. "Toward a More Resilient Financial System: Should Banks Be Diversified?," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    60. Karolina Puławska, 2022. "Effects of the bank levy introduction on the interbank market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 844-864, January.
    61. Bogdan-Gabriel MOINESCU, 2012. "Determinants Of Nonperforming Loans In Central And Eastern European Countries: Macroeconomic Indicators And Credit Discipline," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 10, pages 47-58, December.
    62. Claudia Miani & Giulio Nicoletti & Alessandro Notarpietro & Massimiliano Pisani, 2012. "Banks� balance sheets and the macroeconomy in the Bank of Italy Quarterly Model," Questioni di Economia e Finanza (Occasional Papers) 135, Bank of Italy, Economic Research and International Relations Area.

Articles

  1. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    See citations under working paper version above.
  2. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.
    See citations under working paper version above.
  3. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    See citations under working paper version above.
  4. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
    See citations under working paper version above.
  5. Massoud Metghalchi & Juri Marcucci & Yung-Ho Chang, 2012. "Are moving average trading rules profitable? Evidence from the European stock markets," Applied Economics, Taylor & Francis Journals, vol. 44(12), pages 1539-1559, April.

    Cited by:

    1. Ülkü, Numan & Prodan, Eugeniu, 2013. "Drivers of technical trend-following rules' profitability in world stock markets," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 214-229.
    2. Massoud Metghalchi & Linda A. Hayes & Farhang Niroomand, 2019. "A technical approach to equity investing in emerging markets," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 389-403, July.
    3. Metghalchi, Massoud & Chen, Chien-Ping & Hayes, Linda A., 2015. "History of share prices and market efficiency of the Madrid general stock index," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 178-184.
    4. Karen Balladares & José Pedro Ramos-Requena & Juan Evangelista Trinidad-Segovia & Miguel Angel Sánchez-Granero, 2021. "Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency," Mathematics, MDPI, vol. 9(2), pages 1-20, January.
    5. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    6. Sánchez-Granero, M.A. & Balladares, K.A. & Ramos-Requena, J.P. & Trinidad-Segovia, J.E., 2020. "Testing the efficient market hypothesis in Latin American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    7. Flavio Ivo Riedlinger & João Nicolau, 2020. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 61-81, March.
    8. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
    9. Leković Miljan, 2018. "Evidence for and Against the Validity of Efficient Market Hypothesis," Economic Themes, Sciendo, vol. 56(3), pages 369-387, September.
    10. Nijolė MAKNICKIENĖ & Jelena STANKEVIČIENĖ & Algirdas MAKNICKAS, 2020. "Comparison of Forex Market Forecasting Tools Based on Evolino Ensemble and Technical Analysis Indicators," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 134-148, September.
    11. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).
    12. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    13. Chuang, O-Chia & Chuang, Hui-Ching & Wang, Zixuan & Xu, Jin, 2024. "Profitability of technical trading rules in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    14. Min-Yuh Day & Yensen Ni & Chinning Hsu & Paoyu Huang, 2022. "Do Investment Strategies Matter for Trading Global Clean Energy and Global Energy ETFs?," Energies, MDPI, vol. 15(9), pages 1-15, May.
    15. Ni, Yensen & Day, Min-Yuh & Huang, Paoyu & Yu, Shang-Ru, 2020. "The profitability of Bollinger Bands: Evidence from the constituent stocks of Taiwan 50," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    16. Urquhart, Andrew & Gebka, Bartosz & Hudson, Robert, 2015. "How exactly do markets adapt? Evidence from the moving average rule in three developed markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 127-147.
    17. Jacinta Chan Phooi M’ng & Rozaimah Zainudin, 2016. "Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    18. Ioana-Andreea Boboc & Mihai-Cristian Dinică, 2013. "An Algorithm for Testing the Efficient Market Hypothesis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    19. Farhang Niroomand & Massoud Metghalchi & Massomeh Hajilee, 2020. "Efficient market hypothesis: a ruinous implication for Portugese stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(4), pages 749-763, October.
    20. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    21. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.
    22. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.

  6. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.

    Cited by:

    1. Costello, Greg & Fraser, Patricia & Groenewold, Nicolaas, 2011. "House prices, non-fundamental components and interstate spillovers: The Australian experience," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 653-669, March.
    2. Hristov, Nikolay & Hülsewig, Oliver, 2017. "Unexpected loan losses and bank capital in an estimated DSGE model of the euro area," Journal of Macroeconomics, Elsevier, vol. 54(PB), pages 161-186.
    3. Beltratti, Andrea & Morana, Claudio, 2010. "International house prices and macroeconomic fluctuations," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 533-545, March.
    4. Aisyah Rahman, 2010. "Financing structure and insolvency risk exposure of Islamic banks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 24(4), pages 419-440, December.
    5. Ph. Du Caju & Th. Roelandt & Chr. Van Nieuwenhuyze & M.-D. Zachary, 2014. "Household debt: evolution and distribution," Economic Review, National Bank of Belgium, issue ii, pages 61-81, September.
    6. Simona Malovana & Zaneta Tesarova, 2019. "Banks' Credit Losses and Provisioning over the Business Cycle: Implications for IFRS 9," Working Papers 2019/4, Czech National Bank.
    7. Fausto Pacicco & Luigi Vena & Andrea Venegoni, 2017. "Market Reactions to ECB Policy Innovations: A Cross-Country Analysis," LIUC Papers in Economics 2017-4, Cattaneo University (LIUC).
    8. Tajik, Mohammad & Aliakbari, Saeideh & Ghalia, Thaana & Kaffash, Sepideh, 2015. "House prices and credit risk: Evidence from the United States," Economic Modelling, Elsevier, vol. 51(C), pages 123-135.
    9. Chong, Beng Soon, 2010. "Interest rate deregulation: Monetary policy efficacy and rate rigidity," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1299-1307, June.
    10. Dana Kise¾áková & Alexander Kise¾ák, 2013. "ANALYSIS OF BANKING BUSINESS AND ITS IMPACT ON FINANCIAL STABILITY OF ECONOMIES IN EURO AREA The main objective of this article is to investigate banking business and analyze factors affecting financi," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 8(1), pages 121-131, December.
    11. Jokivuolle, Esa & Pesola, Jarmo & Viren, Matti, 2015. "Why is credit-to-GDP a good measure for setting countercyclical capital buffers?," Journal of Financial Stability, Elsevier, vol. 18(C), pages 117-126.
    12. Le, Huong Nguyen Quynh & Nguyen, Thai Vu Hong & Schinckus, Christophe, 2022. "The role of strategic interactions in risk-taking behavior: A study from asset growth perspective," International Review of Financial Analysis, Elsevier, vol. 82(C).
    13. Bolt, Wilko & de Haan, Leo & Hoeberichts, Marco & van Oordt, Maarten R.C. & Swank, Job, 2012. "Bank profitability during recessions," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2552-2564.
    14. Abuzayed, Bana & Ben Ammar, Mouldi & Molyneux, Philip & Al-Fayoumi, Nedal, 2024. "Corruption, lending and bank performance," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 802-830.
    15. Lee, Shih-Cheng & Lin, Chien-Ting & Yang, Chih-Kai, 2011. "The asymmetric behavior and procyclical impact of asset correlations," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2559-2568, October.
    16. Banerjee, Anurag & Hung, Chi-Hsiou Daniel & Lo, Kai Lisa, 2016. "An anatomy of credit risk transfer between sovereign and financials in the Eurozone crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 102-120.
    17. Li, Leon & Chen, Carl R., 2016. "Analysts' forecast dispersion and stock returns: a panel threshold regression analysis based on conditional limited market participation hypothesis," Finance Research Letters, Elsevier, vol. 18(C), pages 100-107.
    18. Cucinelli, Doriana & Battista, Maria Luisa Di & Marchese, Malvina & Nieri, Laura, 2018. "Credit risk in European banks: The bright side of the internal ratings based approach," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 213-229.
    19. Chen, Minghua & Wu, Ji & Jeon, Bang Nam & Wang, Rui, 2017. "Monetary policy and bank risk-taking: Evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 31(C), pages 116-140.
    20. Grigori Fainstein & Igor Novikov, 2011. "The role of macroeconomic determinants in credit risk measurement in transition country: Estonian example," International Journal of Transitions and Innovation Systems, Inderscience Enterprises Ltd, vol. 1(2), pages 117-137.
    21. Morakinyo Akinola & Muller Colette & Sibanda Mabutho, 2018. "Non-Performing Loans, Banking System and Macroeconomy," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 63(2), pages 67-86, August.
    22. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2016. "Efficient estimation of unconditional capital by Monte Carlo simulation," Finance Research Letters, Elsevier, vol. 16(C), pages 75-84.
    23. Kuo, Chii-Shyan & Li, Ming-Yuan Leon & Yu, Shang-En, 2013. "Non-uniform effects of CEO equity-based compensation on firm performance – An application of a panel threshold regression model," The British Accounting Review, Elsevier, vol. 45(3), pages 203-214.
    24. Dovern, Jonas & Meier, Carsten-Patrick & Vilsmeier, Johannes, 2010. "How resilient is the German banking system to macroeconomic shocks?," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1839-1848, August.
    25. Leon Li & Mark J. Holmes & Bong Soo Lee, 2016. "The asymmetric relationship between executive earnings management and compensation: a panel threshold regression approach," Applied Economics, Taylor & Francis Journals, vol. 48(57), pages 5525-5545, December.
    26. Dana Kiselakova & Beata Sofrankova & Miroslava Soltes, 2016. "Analytical View on the Financial and Social Stability within the Euro Area: Empirical Evidence from Slovakia," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1637-1645.
    27. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2015. "Capital cyclicality, conditional coverage and long-term capital assessment," Finance Research Letters, Elsevier, vol. 15(C), pages 246-256.
    28. Golbabaei, Ali & Botshekan, Mahmoud, 2022. "The capital ratio and the interest rate spread: A panel threshold regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 289-302.
    29. Saleh Alodayni, 2016. "Oil Prices, Credit Risks in Banking Systems, and Macro-Financial Linkages across GCC Oil Exporters," IJFS, MDPI, vol. 4(4), pages 1-14, November.
    30. Alejandro Ferrer Pérez & José Casals Carro & Sonia Sotoca López, 2014. "A new approach to the unconditional measurement of default risk," Documentos de Trabajo del ICAE 2014-11, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    31. Foos, Daniel & Norden, Lars & Weber, Martin, 2010. "Loan growth and riskiness of banks," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 2929-2940, December.
    32. Wang, Rui & Luo, Hang (Robin), 2022. "How does financial inclusion affect bank stability in emerging economies?," Emerging Markets Review, Elsevier, vol. 51(PA).
    33. Akhtar, Shumi & Faff, Robert & Oliver, Barry & Subrahmanyam, Avanidhar, 2011. "The power of bad: The negativity bias in Australian consumer sentiment announcements on stock returns," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1239-1249, May.
    34. Carlos Pérez Montes & Alejandro Ferrer Pérez, 2018. "The impact of the interest rate level on bank profitability and balance sheet structure," Revista de Estabilidad Financiera, Banco de España, issue Otoño.
    35. Mr. Daniel C Hardy & Mr. Christian Schmieder, 2013. "Rules of Thumb for Bank Solvency Stress Testing," IMF Working Papers 2013/232, International Monetary Fund.
    36. Rui Wang & Hang (Robin) Luo, 2019. "Does Financial Liberalization Affect Bank Risk-Taking in China?," SAGE Open, , vol. 9(4), pages 21582440198, November.
    37. Racicot, François-Éric & Théoret, Raymond, 2019. "Hedge fund return higher moments over the business cycle," Economic Modelling, Elsevier, vol. 78(C), pages 73-97.
    38. Jordan Kjosevski & Mihail Petkovski, 2021. "Macroeconomic and bank-specific determinants of non-performing loans: the case of baltic states," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 1009-1028, November.
    39. Isnurhadi & Sulastri & Yulia Saftiana & Ferry Jie, 2022. "Banking Industry Sustainable Growth Rate under Risk: Empirical Study of the Banking Industry in ASEAN Countries," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    40. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    41. J.A. Bikker & Tobias M. Vervliet, 2017. "Bank Profitability and Risk-Taking under Low Interest Rates," Working Papers 17-10, Utrecht School of Economics.
    42. Bui, Duy-Tung & Nguyen, Canh Phuc & Su, Thanh Dinh, 2021. "Asymmetric impacts of monetary policy and business cycles on bank risk-taking: Evidence from Emerging Asian markets," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    43. Jeon, Bang & Wu, Ji & Chen, Minghua & Wang, Rui, 2016. "Do foreign banks take more risk? Evidence from emerging economies," School of Economics Working Paper Series 2016-4, LeBow College of Business, Drexel University.
    44. Pesola, Jarmo, 2011. "Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3134-3144, November.
    45. Akhigbe, Aigbe & Madura, Jeff & Marciniak, Marek, 2012. "Bank capital and exposure to the financial crisis," Journal of Economics and Business, Elsevier, vol. 64(5), pages 377-392.
    46. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2015. "Sample dependency during unconditional credit capital estimation," Finance Research Letters, Elsevier, vol. 15(C), pages 175-186.
    47. Athanasoglou, Panayiotis P. & Daniilidis, Ioannis & Delis, Manthos D., 2014. "Bank procyclicality and output: Issues and policies," Journal of Economics and Business, Elsevier, vol. 72(C), pages 58-83.
    48. Jeon, Bang & Wu, Ji & Chen, Minghua & Wang, Rui, 2016. "Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies," School of Economics Working Paper Series 2016-14, LeBow College of Business, Drexel University.
    49. Ion LAPTEACRU, 2022. "What drives the risk of European banks during crises? New evidence and insights," Bordeaux Economics Working Papers 2022-02, Bordeaux School of Economics (BSE).
    50. Li, Yuming, 2015. "The asymmetric house price dynamics: Evidence from the California market," Regional Science and Urban Economics, Elsevier, vol. 52(C), pages 1-12.
    51. Xue, Wenjun & Zhang, Liwen, 2019. "Revisiting the asymmetric effects of bank credit on the business cycle: A panel quantile regression approach," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    52. Panayiotis P. Athanasoglou & Ioannis Daniilidis, 2011. "Procyclicality in the banking industry: causes, consequences and response," Working Papers 139, Bank of Greece.
    53. Behr, Patrick & Guettler, Andre & Miebs, Felix, 2013. "On portfolio optimization: Imposing the right constraints," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1232-1242.
    54. Zhang, Dayong & Cai, Jing & Dickinson, David G. & Kutan, Ali M., 2016. "Non-performing loans, moral hazard and regulation of the Chinese commercial banking system," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 48-60.
    55. Li, Kunpeng, 2022. "Threshold spatial autoregressive model," MPRA Paper 113568, University Library of Munich, Germany.
    56. Parnes, Dror & Gormus, Alper, 2024. "Prescreening bank failures with K-means clustering: Pros and cons," International Review of Financial Analysis, Elsevier, vol. 93(C).
    57. Antão, Paula & Lacerda, Ana, 2011. "Capital requirements under the credit risk-based framework," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1380-1390, June.
    58. Calmès, Christian & Théoret, Raymond, 2020. "Bank fee-based shocks and the U.S. business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    59. Mihail Petkovski & Jordan Kjosevski & Kiril Jovanovski, 2018. "Empirical Panel Analysis of Non-performing Loans in the Czech Republic. What are their Determinants and How Strong is their Impact on the Real Economy?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 68(5), pages 460-490, October.
    60. Sommervoll, Dag Einar & Borgersen, Trond-Arne & Wennemo, Tom, 2010. "Endogenous housing market cycles," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 557-567, March.
    61. Cicchiello, Antonella Francesca & Cotugno, Matteo & Perdichizzi, Salvatore & Torluccio, Giuseppe, 2022. "Do capital buffers matter? Evidence from the stocks and flows of nonperforming loans," International Review of Financial Analysis, Elsevier, vol. 84(C).
    62. Miroslav Plasil & Tomas Konecny & Jakub Seidler & Petr Hlavac, 2015. "In the Quest of Measuring the Financial Cycle," Working Papers 2015/05, Czech National Bank.
    63. Ion Lapteacru, 2022. "What drives the risk of European banks during crises? New evidence and insights," Working Papers hal-03775463, HAL.
    64. Ferdaous Bahri & Taher Hamza, 2020. "The Impact of Market Power on Bank Risk-Taking: an Empirical Investigation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(3), pages 1198-1233, September.
    65. Jokivuolle, Esa & Pesola, Jarmo & Virén, Matti, 2014. "What drives loan losses in Europe?," Bank of Finland Research Discussion Papers 6/2014, Bank of Finland.
    66. Van Tassel, Eric, 2011. "Information disclosure in credit markets when banks' costs are endogenous," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 490-497, February.
    67. Carlos Pérez Montes & Alejandro Ferrer Pérez, 2018. "The impact of the interest rate level on bank profitability and balance sheet structure," Financial Stability Review, Banco de España, issue Autumn.
    68. Shumi Akhtar & Robert Faff & Barry Oliver, 2011. "The asymmetric impact of consumer sentiment announcements on Australian foreign exchange rates," Australian Journal of Management, Australian School of Business, vol. 36(3), pages 387-403, December.
    69. Ion Lapteacru, 2022. "What drives the risk of European banks during crises? New evidence and insights," Working Papers hal-03625046, HAL.
    70. Veríssimo, Pedro & de Carvalho, Paulo Viegas & Laureano, Luís, 2021. "Asymmetries in the Euro area banking profitability," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).

  7. Marcucci, Juri & Quagliariello, Mario, 2008. "Is bank portfolio riskiness procyclical: Evidence from Italy using a vector autoregression," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(1), pages 46-63, February.
    See citations under working paper version above.
  8. Metghalchi, Massoud & Chang, Yung-Ho & Marcucci, Juri, 2008. "Is the Swedish stock market efficient? Evidence from some simple trading rules," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 475-490, June.

    Cited by:

    1. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    2. Ülkü, Numan & Prodan, Eugeniu, 2013. "Drivers of technical trend-following rules' profitability in world stock markets," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 214-229.
    3. Massoud Metghalchi & Linda A. Hayes & Farhang Niroomand, 2019. "A technical approach to equity investing in emerging markets," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 389-403, July.
    4. Metghalchi, Massoud & Chen, Chien-Ping & Hayes, Linda A., 2015. "History of share prices and market efficiency of the Madrid general stock index," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 178-184.
    5. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi, November.
    6. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    7. Stefanescu, Răzvan & Dumitriu, Ramona, 2015. "Buy and sell signals on Bucharest Stock Exchange," MPRA Paper 89014, University Library of Munich, Germany, revised 05 Jan 2016.
    8. Metghalchi Massoud & Garza-Gomez Xavier, 2011. "Trading Rules for the Abu Dhabi Stock Index," Review of Middle East Economics and Finance, De Gruyter, vol. 7(1), pages 52-66, May.
    9. Graham, Michael & Peltomäki, Jarkko & Sturludóttir, Hildur, 2015. "Do capital controls affect stock market efficiency? Lessons from Iceland," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 82-88.
    10. Kung, James J., 2009. "Predictability of Technical Trading Rules: Evidence from the Taiwan Stock Market," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 5(1-2), pages 1-17, March.
    11. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    12. Yi-Chein Chiang & Mei-Chu Ke & Tung Liang Liao & Cin-Dian Wang, 2012. "Are technical trading strategies still profitable? Evidence from the Taiwan Stock Index Futures Market," Applied Financial Economics, Taylor & Francis Journals, vol. 22(12), pages 955-965, June.
    13. Farhang Niroomand & Massoud Metghalchi & Massomeh Hajilee, 2020. "Efficient market hypothesis: a ruinous implication for Portugese stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(4), pages 749-763, October.
    14. Eero P䴤ri & Mika Vilska, 2014. "Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2851-2872, August.
    15. Gerritsen, Dirk F., 2016. "Are chartists artists? The determinants and profitability of recommendations based on technical analysis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 179-196.

  9. Lotti, Francesca & Marcucci, Juri, 2007. "Revisiting the empirical evidence on firms' money demand," Journal of Economics and Business, Elsevier, vol. 59(1), pages 51-73.

    Cited by:

    1. Bafile, Romina & Piergallini, Alessandro, 2011. "Firms’ Money Demand and Monetary Policy," MPRA Paper 29028, University Library of Munich, Germany.
    2. Luca Sessa, 2012. "Economic (in)stability under monetary targeting," Temi di discussione (Economic working papers) 858, Bank of Italy, Economic Research and International Relations Area.
    3. Fangping Peng & R. J. Cebula & M. Foley & Kai Zhan, 2016. "Estimation of the liquidity trap using a panel threshold model," Applied Economics Letters, Taylor & Francis Journals, vol. 23(16), pages 1134-1137, November.

  10. Sebastiano Laviola & Juri Marcucci & Mario Quagliariello, 2006. "Stress testing credit risk: experience from the italian FSAP," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 59(238), pages 269-291.

    Cited by:

    1. Antonella Foglia, 2008. "Stress testing credit risk: a survey of authorities' approaches," Questioni di Economia e Finanza (Occasional Papers) 37, Bank of Italy, Economic Research and International Relations Area.
    2. Antonella Foglia, 2009. "Stress Testing Credit Risk: A Survey of Authorities' Aproaches," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 9-45, September.

  11. Engle, Robert F. & Marcucci, Juri, 2006. "A long-run Pure Variance Common Features model for the common volatilities of the Dow Jones," Journal of Econometrics, Elsevier, vol. 132(1), pages 7-42, May.

    Cited by:

    1. Gianluca Cubadda & Barbara Guardabascio & Alain Hecq, 2016. "A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures," CEIS Research Paper 391, Tor Vergata University, CEIS, revised 23 Jul 2016.
    2. Doseong Kim & Yoon-Goo Lee & Isabel Ruiz, 2010. "Common Volatility: An Empirical Investigation of Closed-End Country Funds," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 116-132, March.
    3. Hecq Alain & Laurent Sébastien & Palm Franz C., 2016. "On the Univariate Representation of BEKK Models with Common Factors," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 91-113, July.
    4. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2013. "Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown," MPRA Paper 49344, University Library of Munich, Germany.
    5. Matteo Barigozzi & Marc Hallin, 2014. "Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks," Working Papers ECARES ECARES 2014-52, ULB -- Universite Libre de Bruxelles.
    6. Carlos E. da Costa & Jaime de Jesus Filho & Paulo Matos, 2016. "Forward-premium puzzle: is it time to abandon the usual regression?," Applied Economics, Taylor & Francis Journals, vol. 48(30), pages 2852-2867, June.
    7. J. Piplack & M. Beine & B. Candelon, 2009. "Comovements of Returns and Volatility in International Stock Markets: A High-Frequency Approach," Working Papers 09-10, Utrecht School of Economics.
    8. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    9. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    10. Marco Centoni & Gianluca Cubadda, 2011. "Modelling Comovements of Economic Time Series: A Selective Survey," CEIS Research Paper 215, Tor Vergata University, CEIS, revised 26 Oct 2011.
    11. Gian Piero Aielli & Massimiliano Caporin, 2011. "Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators," "Marco Fanno" Working Papers 0133, Dipartimento di Scienze Economiche "Marco Fanno".
    12. Dovonon, Prosper & Renault, Eric, 2011. "Testing for Common GARCH Factors," MPRA Paper 40224, University Library of Munich, Germany.
    13. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    14. Yang Gao & Bianxia Sun, 2018. "Impacts of Introducing Index Futures on Stock Market Volatilities: New Evidences from China," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-23, December.
    15. Alain Hecq & Sébastien Laurent & Franz C. Palm, 2011. "Common Intraday Periodicity," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 325-353, 2012 20 1.
    16. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    17. Schaeffer, Roberto & Borba, Bruno S.M.C. & Rathmann, Régis & Szklo, Alexandre & Castelo Branco, David A., 2012. "Dow Jones sustainability index transmission to oil stock market returns: A GARCH approach," Energy, Elsevier, vol. 45(1), pages 933-943.
    18. Matteo Barigozzi & Marc Hallin, 2015. "Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting," Working Papers ECARES ECARES 2015-22, ULB -- Universite Libre de Bruxelles.
    19. Kai Wu & Yi Liu & Weiyang Feng, 2022. "The Effect of Index Option Trading on Stock Market Volatility in China: An Empirical Investigation," JRFM, MDPI, vol. 15(4), pages 1-19, March.
    20. Heather Anderson & Fashid Vahid, 2005. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," ANU Working Papers in Economics and Econometrics 2005-451, Australian National University, College of Business and Economics, School of Economics.
    21. Barigozzi, Matteo & Hallin, Marc, 2020. "Generalized dynamic factor models and volatilities: Consistency, rates, and prediction intervals," Journal of Econometrics, Elsevier, vol. 216(1), pages 4-34.
    22. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Papers 1510.05118, arXiv.org, revised Jul 2016.
    23. Hecq, A.W. & Laurent, S.F.J.A. & Palm, F.C., 2011. "On the univariate representation of multivariate volatility models with common factors," Research Memorandum 011, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    24. George Athanasopoulos & Heather M. Anderson & Farshid Vahid, 2007. "Nonlinear autoregressive leading indicator models of output in G-7 countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 63-87.
    25. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
    26. Wamg, Jianxin, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.

  12. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    3. Roberto Ferulano, 2009. "A Mixed Historical Formula to forecast volatility," Journal of Asset Management, Palgrave Macmillan, vol. 10(2), pages 124-136, June.
    4. Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
    5. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    6. Luc, BAUWENS & G., STORTI, 2007. "A Component GARCH Model with Time Varying Weights," Discussion Papers (ECON - Département des Sciences Economiques) 2007012, Université catholique de Louvain, Département des Sciences Economiques.
    7. Reza, Md. Ridwan & Masih, Mansur, 2017. "Regime switching behavior of volatilities of Islamic equities: evidence from Markov- Switching GARCH models for some selected broad based indices," MPRA Paper 82123, University Library of Munich, Germany.
    8. P. Sattayatham & N. Sopipan & B. Premanode, 2012. "Forecasting the Stock Exchange of Thailand uses Day of the Week Effect and Markov Regime Switching GARCH," American Journal of Economics and Business Administration, Science Publications, vol. 4(1), pages 84-93, March.
    9. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    10. Daglis, Theodoros & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Papadakis, Theodoulos Eleftherios, 2020. "The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    11. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    12. Richard Hawkes & Paresh Date, 2007. "Medium‐term horizon volatility forecasting: A comparative study," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(6), pages 465-481, November.
    13. Jean Marcelin B. Brou & Mbodja Mougoué & Eugene Kouassi & Kebaabetswe Thulaganyo & Benjamin K. Acquah, 2022. "Effects of diamond price volatility on stock returns: Evidence from a developing economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1025-1043, January.
    14. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    15. N. Chitra Devi & S. Chandramohan, 2016. "Asymmetric relationship between stock market returns and macroeconomic variables," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 79-94.
    16. Syed Abul, Basher & Alfred A, Haug & Perry, Sadorsky, 2015. "The impact of oil shocks on exchange rates: A Markov-switching approach," MPRA Paper 68232, University Library of Munich, Germany.
    17. A. Gabrielsen & P. Zagaglia & A. Kirchner & Z. Liu, 2012. "Forecasting Value-at-Risk with Time-Varying Variance, Skewness and Kurtosis in an Exponential Weighted Moving Average Framework," Papers 1206.1380, arXiv.org.
    18. Samet Günay, 2016. "Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets," IJFS, MDPI, vol. 4(2), pages 1-17, May.
    19. Heidari , Hassan & Refah-Kahriz, Arash & Hashemi Berenjabadi, Nayyer, 2018. "Dynamic Relationship between Macroeconomic Variables and Stock Return Volatility in Tehran Stock Exchange: Multivariate MS ARMA GARCH Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 5(2), pages 223-250, August.
    20. Cheng Peng & Young Shin Kim & Stefan Mittnik, 2020. "Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation," Papers 2009.11367, arXiv.org, revised Feb 2023.
    21. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    22. M. Marzo & P. Zagaglia, 2007. "Domestic political constraints to foreign aid effectiveness," Working Papers 599, Dipartimento Scienze Economiche, Universita' di Bologna.
    23. King, Daniel & Botha, Ferdi, 2015. "Modelling stock return volatility dynamics in selected African markets," Economic Modelling, Elsevier, vol. 45(C), pages 50-73.
    24. Junru Zhang & Hadrian Geri Djajadikerta & Zhaoyong Zhang, 2018. "Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    25. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    26. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    27. Chang, Kuang-Liang & Yu, Shih-Ti, 2013. "Does crude oil price play an important role in explaining stock return behavior?," Energy Economics, Elsevier, vol. 39(C), pages 159-168.
    28. Iulian Lolea, 2017. "Where did the GARCH Models Perform Best in Terms of Volatility Forecasting? Equity vs. Commodities Markets," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 3(3), pages 79-86, September.
    29. Carol Alexander & Emese Lazar & Silvia Stanescu, 2018. "Analytic Moments for GARCH Processes," Papers 1808.09666, arXiv.org, revised Sep 2018.
    30. Feng Lingbing & Shi Yanlin, 2020. "Markov regime-switching autoregressive model with tempered stable distribution: simulation evidence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-27, February.
    31. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
    32. Chang, Kuang-Liang, 2016. "Does the return-state-varying relationship between risk and return matter in modeling the time series process of stock return?," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 72-87.
    33. Su, EnDer, 2013. "Stock index hedge using trend and volatility regime switch model considering hedging cost," MPRA Paper 49190, University Library of Munich, Germany.
    34. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    35. Monica Billio & Roberto Casarin & Anthony Osuntuyi, 2012. "Efficient Gibbs Sampling for Markov Switching GARCH Models," Working Papers 2012:35, Department of Economics, University of Venice "Ca' Foscari".
    36. Chappell, Daniel, 2018. "Regime heteroskedasticity in Bitcoin: A comparison of Markov switching models," MPRA Paper 90682, University Library of Munich, Germany.
    37. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    38. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    39. contact_cb@yahoo.com. & Simona STAMULE & Iulian Cornel LOLEA, 2021. "The Spillover Effect on the CEE Equity Markets and the Financial Contagion in the Context of Financial Integration," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 155-170, December.
    40. Gębka, Bartosz & Serwa, Dobromił, 2015. "The elusive nature of motives to trade: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 147-157.
    41. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    42. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    43. Riccardo De Blasis & Filippo Petroni, 2021. "Price Leadership and Volatility Linkages between Oil and Renewable Energy Firms during the COVID-19 Pandemic," Energies, MDPI, vol. 14(9), pages 1-16, May.
    44. Ra l de Jes s-Guti rrez & Roberto J. Santill n-Salgado, 2019. "Conditional Extreme Values Theory and Tail-related Risk Measures: Evidence from Latin American Stock Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 127-141.
    45. Su, EnDer, 2017. "Stock index hedging using a trend and volatility regime-switching model involving hedging cost," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 233-254.
    46. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    47. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    48. Charfeddine, Lanouar, 2016. "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis," Economic Modelling, Elsevier, vol. 53(C), pages 354-374.
    49. Ichkitidze, Yuri, 2018. "Temporary price trends in the stock market with rational agents," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 103-117.
    50. Massimiliano Marzo & Paolo Zagaglia, 2010. "Volatility forecasting for crude oil futures," Applied Economics Letters, Taylor & Francis Journals, vol. 17(16), pages 1587-1599.
    51. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    52. Yanlin Shi & Lingbing Feng & Tong Fu, 2020. "Markov Regime-Switching in-Mean Model with Tempered Stable Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1275-1299, April.
    53. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    54. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    55. Rehman, Mobeen Ur, 2019. "Energy shocks pricing model: A non-linear US sectoral based analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    56. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    57. Lolea Iulian Cornel & Stamule Simona, 2021. "Trading using Hidden Markov Models during COVID-19 turbulences," Management & Marketing, Sciendo, vol. 16(4), pages 334-351, December.
    58. Zieling, Daniel & Mahayni, Antje & Balder, Sven, 2014. "Performance evaluation of optimized portfolio insurance strategies," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 212-225.
    59. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    60. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    61. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    62. Xiao, Yang, 2020. "The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 173-186.
    63. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    64. Roberta Colavecchio & Michael Funke, 2007. "Volatility dependence across Asia-Pacific on-shore and off-shore U.S. dollar futures markets," Quantitative Macroeconomics Working Papers 20708, Hamburg University, Department of Economics.
    65. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    66. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    67. Subhranginee Das & Sarat Chandra Nayak & Biswajit Sahoo, 2022. "Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 1-23, June.
    68. Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
    69. Alizadeh, Amir H. & Gabrielsen, Alexandros, 2013. "Dynamics of credit spread moments of European corporate bond indexes," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3125-3144.
    70. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    71. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).
    72. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    73. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    74. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    75. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    76. Kim, Yujin & Hwang, Eunju, 2018. "A dynamic Markov regime-switching GARCH model and its cumulative impulse response function," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 20-30.
    77. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
    78. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    79. Liu, Yue & Tian, Lixin & Sun, Huaping & Zhang, Xiling & Kong, Chuimin, 2022. "Option pricing of carbon asset and its application in digital decision-making of carbon asset," Applied Energy, Elsevier, vol. 310(C).
    80. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    81. D’Amico, Guglielmo & Gismondi, Fulvio & Petroni, Filippo & Prattico, Flavio, 2019. "Stock market daily volatility and information measures of predictability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 22-29.
    82. Raihan, Tasneem, 2017. "Performance of Markov-Switching GARCH Model Forecasting Inflation Uncertainty," MPRA Paper 82343, University Library of Munich, Germany.
    83. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    84. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    85. Hyun Kook Shin & Byoung Hark Yoo, 2012. "The Volatility Of The Won-Dollar Exchange Rate During The 2008-9 Crisis," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 37(4), pages 61-77, December.
    86. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    87. Abounoori, Esmaiel & Elmi, Zahra (Mila) & Nademi, Younes, 2016. "Forecasting Tehran stock exchange volatility; Markov switching GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 264-282.
    88. Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
    89. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    90. Lolea Iulian-Cornel & Vilcu Lucian Constantin, 2018. "Measures of volatility for the Romanian Stock Exchange: a regime switching approach," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 12(1), pages 544-556, May.
    91. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," IJFS, MDPI, vol. 2(4), pages 1-20, October.
    92. Chang, Kuang-Liang, 2022. "Do economic policy uncertainty indices matter in joint volatility cycles between U.S. and Japanese stock markets?," Finance Research Letters, Elsevier, vol. 47(PA).
    93. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    94. Viktor Manahov & Robert Hudson, 2013. "New Evidence of Technical Trading Profitability," Economics Bulletin, AccessEcon, vol. 33(4), pages 2493-2503.
    95. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
    96. Philippe Charlot & Vêlayoudom Marimoutou, 2008. "Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model," Working Papers halshs-00285866, HAL.
    97. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    98. Güngör Turan & Gjana Ima, 2021. "The Link Between Innovation Behaviors and Productivity Strategies of Enterprises in Albanian Economic Growth," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 1, September.
    99. Sajjad Rasoul & Coakley Jerry & Nankervis John C, 2008. "Markov-Switching GARCH Modelling of Value-at-Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-31, September.
    100. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    101. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    102. Charfeddine, Lanouar, 2014. "True or spurious long memory in volatility: Further evidence on the energy futures markets," Energy Policy, Elsevier, vol. 71(C), pages 76-93.
    103. Dejan Zivkov & Marina Gajic-Glamoclija & Jelena Kovacevic & Sanja Loncar, 2020. "Inflation Uncertainty and Output Growth - Evidence from the Asia-Pacific Countries Based on the Multiscale Bayesian Quantile Inference," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(5), pages 461-486, November.
    104. Jacques Jaussaud & Sophie Nivoix & Serge Rey, 2015. "The Great East Japan Earthquake and Stock Prices," Economics Bulletin, AccessEcon, vol. 35(2), pages 1237-1261.
    105. En-Der Su & Feng-Jeng Lin, 2012. "Two-State Volatility Transition Pricing and Hedging of TXO Options," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 259-287, March.
    106. Abdessamad Ouchen, 2022. "Is the ESG portfolio less turbulent than a market benchmark portfolio?," Risk Management, Palgrave Macmillan, vol. 24(1), pages 1-33, March.
    107. Mehdi Zolfaghari & Bahram Sahabi, 2021. "The impact of oil price and exchange rate on momentum strategy profits in stock market: evidence from oil-rich developing countries," Review of Managerial Science, Springer, vol. 15(7), pages 1981-2023, October.
    108. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    109. Wu, Pei-Shan & Huang, Chien-Ming & Chiu, Chien-Liang, 2011. "Effects of structural changes on the risk characteristics of REIT returns," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 645-653, October.
    110. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    111. Liu, Yue & Sun, Huaping & Zhang, Jijian & Taghizadeh-Hesary, Farhad, 2020. "Detection of volatility regime-switching for crude oil price modeling and forecasting," Resources Policy, Elsevier, vol. 69(C).
    112. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    113. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    114. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
    115. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    116. Escañuela Romana, Ignacio, 2011. "Evidencia empírica sobre la predictibilidad de los ciclos bursátiles: el comportamiento del índice Dow Jones Industrial Average en las crisis bursátiles de 1929, 1987 y 2997 [Empirical evidence on ," MPRA Paper 33150, University Library of Munich, Germany.
    117. Alexander Zeitlberger & Alexander Brauneis, 2016. "Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 149-176, March.
    118. Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.
    119. Yoo Byoung Hark, 2010. "Estimating the Term Premium by a Markov Switching Model with ARMA-GARCH Errors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-20, March.
    120. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    121. Pappas, Vasileios & Ingham, Hilary & Izzeldin, Marwan & Steele, Gerry, 2016. "Will the crisis “tear us apart”? Evidence from the EU," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 346-360.
    122. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, vol. 18(C), pages 123-140.
    123. Danial Saef & Yuanrong Wang & Tomaso Aste, 2022. "Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing," Papers 2208.12614, arXiv.org, revised Sep 2022.
    124. Marie-Eliette Dury & Bing Xiao, 2018. "Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models," Working Papers hal-01709321, HAL.
    125. BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.
    126. Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024. "Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model," Finance Research Letters, Elsevier, vol. 64(C).
    127. Heitham Al-Hajieh & Hashem AlNemer & Timothy Rodgers & Jacek Niklewski, 2015. "Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 4(2), pages 9-26.
    128. Hao Wu & Haiming Long & Yue Wang & Yanqi Wang, 2021. "Stock index forecasting: A new fuzzy time series forecasting method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 653-666, July.
    129. Levy, Moshe & Kaplanski, Guy, 2015. "Portfolio selection in a two-regime world," European Journal of Operational Research, Elsevier, vol. 242(2), pages 514-524.
    130. Yue-Jun Zhang & Ting Yao & Ling-Yun He, 2015. "Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?," Papers 1512.01676, arXiv.org.
    131. Pradosh Simlai, 2012. "Endogenous Information, Risk Characterization, and the Predictability of Average Stock Returns," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 291-315.

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