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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. 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.
    2. 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.
    3. 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.
    4. De Bandt Olivier & Bricongne Jean-Charles & Denes Julien & Dhenin Alexandre & De Gaye Annabelle & Robert Pierre-Antoine, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

  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. 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.
    2. 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.
    3. 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.
    4. Xinyu Li & Zihan Tang, 2022. "Sentiment Analysis on Inflation after Covid-19," Papers 2209.14737, arXiv.org, revised Dec 2022.
    5. De Bandt Olivier & Bricongne Jean-Charles & Denes Julien & Dhenin Alexandre & De Gaye Annabelle & Robert Pierre-Antoine, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.
    6. 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.
    7. 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.
    8. Donato Masciandaro & Davide Romelli & Gaia Rubera, 2021. "Monetary policy and financial markets: evidence from Twitter traffic," BAFFI CAREFIN Working Papers 21160, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    9. Travis Adams & Andrea Ajello & Diego Silva & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Papers 2305.16164, arXiv.org.
    10. 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.
    11. 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.
    12. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.
    13. 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.
    14. Vivian Chu & Tatjana Dahlhaus & Christopher Hajzler & Pierre-Yves Yanni, 2023. "Digitalization: Implications for Monetary Policy," Discussion Papers 2023-18, Bank of Canada.
    15. 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.).
    16. 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.
    17. 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.).
    18. 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.
    19. Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
    20. 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.
    21. 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).
    22. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    23. 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.
    24. 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. 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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Efrem Castelnuovo & Trung Duc Tran, 2017. "Google It Up! A Google Trends-Based Uncertainty Index for the United States and Australia," Melbourne Institute Working Paper Series wp2017n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    14. 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.
    15. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    16. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    22. 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).
    23. 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.
    24. 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.
    25. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    26. 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.
    27. Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
    28. 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).
    29. 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.
    30. Jung, Alexander & Kühl, Patrick, 2021. "Can central bank communication help to stabilise inflation expectations?," Working Paper Series 2547, European Central Bank.
    31. 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.
    32. Matteo Iacopini & Carlo R. M. A. Santagiustina, 2020. "Filtering the intensity of public concern from social media count data with jumps," Papers 2012.13267, arXiv.org.
    33. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2018. "Volatility persistence and asymmetry under the microscope: The role of information demand for gold and oil," Working Paper series 18-13, Rimini Centre for Economic Analysis.
    34. 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.
    35. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    36. Eric Bax, 2019. "Computing a Data Dividend," Papers 1905.01805, arXiv.org, revised Jun 2019.
    37. 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.
    38. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
    39. 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.
    40. 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.
    41. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper 102315, University Library of Munich, Germany.
    42. 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.
    43. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205161, HAL.
    44. 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.
    45. 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.
    46. Robin Niesert & Jochem Oorschot & Chris Veldhuisen & Kester Brons & Rutger-Jan Lange, "undated". "Can Google Search Data Help Predict Macroeconomic Series?," Tinbergen Institute Discussion Papers 19-021/III, Tinbergen Institute.
    47. 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.
    48. Ilias Georgakopoulos, 2019. "Income and wealth inequality in Malta: evidence from micro data," CBM Working Papers WP/03/2019, Central Bank of Malta.
    49. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    50. 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).
    51. Per Nymand-Andersen, 2016. "Big data: the hunt for timely insights and decision certainty," IFC Working Papers 14, Bank for International Settlements.
    52. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving : Evidence from SMS Donations," The Warwick Economics Research Paper Series (TWERPS) 1325, University of Warwick, Department of Economics.
    53. 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.
    54. 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).
    55. 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).
    56. 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.
    57. 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.
    58. Ferriani, Fabrizio & Gazzani, Andrea, 2022. "Financial condition indices for emerging market economies: Can Google help?," Economics Letters, Elsevier, vol. 216(C).
    59. 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.
    60. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    61. 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.
    62. 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.
    63. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
    64. 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.
    65. 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.
    66. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    67. 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.
    68. 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.
    69. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    70. 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.
    71. 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.
    72. 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).
    73. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.
    74. 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.
    75. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    76. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    77. 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.
    78. 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.
    79. 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.
    80. 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.
    81. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    82. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    83. 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).
    84. 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.
    85. Mikhaylov, Dmitry, 2023. "Macroeconomic Forecasting with the Use of News Data," Working Papers w20220250, Russian Presidential Academy of National Economy and Public Administration.
    86. 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.
    87. 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.
    88. 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.
    89. 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.
    90. 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.
    91. Matteo Iacopini & Carlo Romano Marcello Alessandro Santagiustina, 2021. "Filtering the Intensity of Public Concern from Social Media Count Data with Jumps," Post-Print hal-04494229, HAL.
    92. 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.
    93. Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
    94. 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.
    95. 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.
    96. 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).
    97. 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).
    98. 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).
    99. 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.
    100. 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).
    101. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    102. 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).
    103. 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).
    104. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    105. 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.
    106. 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.
    107. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    108. 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.
    109. 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).
    110. Reuben Ellul, 2018. "Forecasting unemployment rates in Malta: A labour market flows approach," CBM Working Papers WP/03/2018, Central Bank of Malta.
    111. 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.
    112. 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.
    113. 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.
    114. 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.

  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. 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.
    2. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    3. Naraidoo, Ruthira & Paya, Ivan, 2012. "Forecasting monetary policy rules in South Africa," International Journal of Forecasting, Elsevier, vol. 28(2), pages 446-455.
    4. 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.
    5. Graziano Moramarco, 2021. "Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States," Papers 2111.00822, arXiv.org, revised Jan 2024.
    6. 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).
    7. 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.
    8. Chue, Timothy K. & Xu, Jin Karen, 2022. "Profitability, asset investment, and aggregate stock returns," Journal of Banking & Finance, Elsevier, vol. 143(C).
    9. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • 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.
    10. Guillen, Osmani Teixeira Carvalho & Hecq, Alain & Issler, João Victor & Saraiva, Diogo Vinícius Menezes, 2014. "Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 753, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    11. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers (Old Series) 1120, Federal Reserve Bank of Cleveland.
    12. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    13. D'Amuri, Francesco/FD & Marcucci, Juri/JM, 2009. ""Google it!" Forecasting the US unemployment rate with a Google job search index," MPRA Paper 18248, University Library of Munich, Germany.
    14. 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.
    15. Pincheira, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2017. "Forecasting Inflation in Latin America with Core Measures," MPRA Paper 80496, University Library of Munich, Germany.
    16. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    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. 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.
    19. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. Ruthira Naraidoo & Ivan Paya, 2010. "Forecasting Monetary Rules in South Africa," Working Papers 201007, University of Pretoria, Department of Economics.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    32. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    33. 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).
    34. Caruso, Alberto, 2018. "Nowcasting with the help of foreign indicators: The case of Mexico," Economic Modelling, Elsevier, vol. 69(C), pages 160-168.
    35. Li Guo & Lin Peng & Yubo Tao & Jun Tu, 2017. "Joint News, Attention Spillover,and Market Returns," Papers 1703.02715, arXiv.org, revised Nov 2022.
    36. 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..
    37. Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.

  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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    8. Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higa & Fabrice Murtin & Claudia Senik, 2016. "Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States," OECD Statistics Working Papers 2016/3, OECD Publishing.
    9. 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.
    10. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    11. 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.
    12. Pan, Wei-Fong, 2019. "Building sectoral job search indices for the United States," Economics Letters, Elsevier, vol. 180(C), pages 89-93.
    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. 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.
    15. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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.
    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. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
    23. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
    24. 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.
    25. 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.
    26. 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).
    27. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
    28. 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.
    29. 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.
    30. 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.
    31. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
    32. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute of Labor Economics (IZA).
    33. 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.
    34. 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.
    35. 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.
    36. Maria De Paola & Vincenzo Scoppa, 2010. "Consumers’ Reactions To Negative Information On Product Quality: Evidence From Scanner Data," Working Papers 201012, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    37. 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.
    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. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    40. 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.
    41. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
    42. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    43. 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.
    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. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. Florian Schaffner, 2015. "Predicting US bank failures with internet search volume data," ECON - Working Papers 214, Department of Economics - University of Zurich.
    51. 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.
    52. 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.

  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 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    7. 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.
    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. 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.
    2. Sauro Mocetti, 2008. "Educational choices and the selection process before and after compulsory schooling," Temi di discussione (Economic working papers) 691, Bank of Italy, Economic Research and International Relations Area.

  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. 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. Alessandra Canepa & Fawaz Khaled, 2018. "Housing, Housing Finance and Credit Risk," IJFS, MDPI, vol. 6(2), pages 1-23, May.
    3. Inessa Love & Rima Turk Ariss, 2013. "Macro-Financial Linkages in Egypt: A Panel Analysis of Economic Shocks and Loan Portfolio Quality," Working Papers 201310, University of Hawaii at Manoa, Department of Economics.
    4. Zedginidze Zviad, 2012. "Linking Macroeconomic Dynamics to Georgian Credit Portfolio Risk," EERC Working Paper Series 12/07e, EERC Research Network, Russia and CIS.
    5. 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), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 10(1), pages 60-71, March.
    6. Ruja, Catalin, 2014. "Macro Stress-Testing Credit Risk in Romanian Banking System," MPRA Paper 58244, University Library of Munich, Germany.
    7. Baselga-Pascual, Laura & Vähämaa, Emilia, 2021. "Female leadership and bank performance in Latin America," Emerging Markets Review, Elsevier, vol. 48(C).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Stefano Puddu, 2013. "Optimal Weights and Stress Banking Indexes," IRENE Working Papers 13-02, IRENE Institute of Economic Research.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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).
    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. 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.
    27. 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.
    28. 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), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(1), pages 129-143, April.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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).
    36. 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.
    37. 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.
    38. Sreejata Banerjee & Divya Murali, 2015. "Stress Test of Banks in India: A VAR Approach," Working Papers 2015-102, Madras School of Economics,Chennai,India.
    39. Morone, Marco & Cornaglia, Anna, 2010. "An econometric model to quantify benchmark downturn LGD on residential mortgages," MPRA Paper 25588, University Library of Munich, Germany.
    40. 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.
    41. 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.
    42. International Monetary Fund, 2010. "Colombia: Selected Issues Paper," IMF Staff Country Reports 2010/106, International Monetary Fund.
    43. 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.
    44. Rui Pascoal, 2012. "Macroeconomic Factors of Household Default. Is There Myopic Behaviour?," GEMF Working Papers 2012-20, GEMF, Faculty of Economics, University of Coimbra.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. V. Chiorazzo & V. D’Apice & P. Morelli & Giovanni W. Puopolo, 2015. "Economic Activity and Credit Market Linkages: New Evidence from Italy," CSEF Working Papers 413, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    50. 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.
    51. Ms. Mwanza Nkusu, 2011. "Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies," IMF Working Papers 2011/161, International Monetary Fund.
    52. 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.
    53. 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.
    54. 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.
    55. Miyajima Ken, 2017. "An Empirical Investigation of Oil-Macro-financial Linkages in Saudi Arabia," Review of Middle East Economics and Finance, De Gruyter, vol. 13(2), pages 1-15, August.
    56. 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.
    57. 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).
    58. 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".
    59. 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.
    60. Panayiotis P. Athanasoglou & Ioannis Daniilidis, 2011. "Procyclicality in the banking industry: causes, consequences and response," Working Papers 139, Bank of Greece.
    61. 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.

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. 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.
    3. 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.
    4. 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).
    5. 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.
    6. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    7. 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.
    8. Leković Miljan, 2018. "Evidence for and Against the Validity of Efficient Market Hypothesis," Economic Themes, Sciendo, vol. 56(3), pages 369-387, September.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.

  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. Beltratti, Andrea & Morana, Claudio, 2010. "International house prices and macroeconomic fluctuations," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 533-545, March.
    2. 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.
    3. 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.
    4. Racicot, François-Éric & Théoret, Raymond, 2019. "Hedge fund return higher moments over the business cycle," Economic Modelling, Elsevier, vol. 78(C), pages 73-97.
    5. 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.
    6. 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).
    7. 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.
    8. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2015. "Sample dependency during unconditional credit capital estimation," Finance Research Letters, Elsevier, vol. 15(C), pages 175-186.
    9. Miroslav Plasil & Tomas Konecny & Jakub Seidler & Petr Hlavac, 2015. "In the Quest of Measuring the Financial Cycle," Working Papers 2015/05, Czech National Bank.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Mr. Daniel C Hardy & Mr. Christian Schmieder, 2013. "Rules of Thumb for Bank Solvency Stress Testing," IMF Working Papers 2013/232, International Monetary Fund.
    19. 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).
    20. 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.
    21. 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.
    22. Ion Lapteacru, 2022. "What drives the risk of European banks during crises? New evidence and insights," Working Papers hal-03775463, HAL.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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).
    32. 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.
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    51. 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.
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    53. 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).
    54. 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.
    55. 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.
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  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.
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  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.

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    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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. 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.
    14. 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.

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    1. 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.
    2. Bafile, Romina & Piergallini, Alessandro, 2011. "Firms’ Money Demand and Monetary Policy," MPRA Paper 29028, University Library of Munich, Germany.
    3. 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.

  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.

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    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.

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    1. 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.
    2. 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.
    3. Alain Hecq & Franz C. Palm & Sébastien Laurent, 2016. "On the Univariate Representation of BEKK Models with Common Factors," Post-Print hal-01440307, HAL.
    4. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    5. 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).
    6. Dovonon, Prosper & Renault, Eric, 2011. "Testing for Common GARCH Factors," MPRA Paper 40224, University Library of Munich, Germany.
    7. 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.
    8. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    9. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    10. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    11. 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.
    12. Barigozzi, Matteo & Hallin, Mark, 2015. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," LSE Research Online Documents on Economics 60980, London School of Economics and Political Science, LSE Library.
    13. 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.
    14. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
    15. 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.
    16. 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.
    17. 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.
    18. Fabrizio Cipollini & Giampiero M. Gallo, 2018. "Modeling Euro STOXX 50 Volatility with Common and Market–specific Components," Working Paper series 18-26, Rimini Centre for Economic Analysis.
    19. Hecq, A.W. & Palm, F.C. & Laurent, S.F.J.A., 2011. "Common intraday periodicity," Research Memorandum 010, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    20. 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.
    21. 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.
    22. Aielli, Gian Piero & Caporin, Massimiliano, 2014. "Variance clustering improved dynamic conditional correlation MGARCH estimators," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 556-576.
    23. 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.
    24. 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.
    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. 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.
    3. 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.
    4. 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.
    5. 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".
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. Cheng Peng & Young Shin Kim & Stefan Mittnik, 2022. "Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation," JRFM, MDPI, vol. 15(5), pages 1-23, May.
    16. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    17. 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.
    18. Jacques Jaussaud & Sophie Nivoix & Serge Rey, 2015. "The Great East Japan Earthquake and Stock Prices," Post-Print hal-01885285, HAL.
    19. 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.
    20. Marzo, Massimiliano & Zagaglia, Paolo, 2007. "Volatility forecasting for crude oil futures," Research Papers in Economics 2007:9, Stockholm University, Department of Economics.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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).
    28. 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.
    29. 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).
    30. 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).
    31. 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).
    32. 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.
    33. Daniel King & Ferdi Botha, 2014. "Modelling Stock Return Volatility Dynamics in Selected African Markets," Working Papers 410, Economic Research Southern Africa.
    34. Carol Alexander & Emese Lazar & Silvia Stanescu, 2010. "Analytic Moments for GARCH Processes," ICMA Centre Discussion Papers in Finance icma-dp2011-07, Henley Business School, University of Reading, revised Apr 2011.
    35. Levy, Moshe & Kaplanski, Guy, 2015. "Portfolio selection in a two-regime world," European Journal of Operational Research, Elsevier, vol. 242(2), pages 514-524.
    36. 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.
    37. 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.
    38. 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.
    39. M. Marzo & P. Zagaglia, 2007. "Domestic political constraints to foreign aid effectiveness," Working Papers 599, Dipartimento Scienze Economiche, Universita' di Bologna.
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