Daniel Borup
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First Name: | Daniel |
Middle Name: | |
Last Name: | Borup |
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RePEc Short-ID: | pbo994 |
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Affiliation
Center for Research in Econometric Analysis of Time Series (CREATES)
Institut for Økonomi
Aarhus Universitet
Aarhus, Denmarkhttp://www.creates.au.dk/
RePEc:edi:creaudk (more details at EDIRC)
Research output
Jump to: Working papers ArticlesWorking papers
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022.
"The Anatomy of Out-of-Sample Forecasting Accuracy,"
FRB Atlanta Working Paper
2022-16, Federal Reserve Bank of Atlanta.
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
- 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.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020.
"Targeting predictors in random forest regression,"
CREATES Research Papers
2020-03, Department of Economics and Business Economics, Aarhus University.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020.
"Predicting bond return predictability,"
CREATES Research Papers
2020-09, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
- Daniel Borup & Erik Christian Montes Schütte, 2019.
"In search of a job: Forecasting employment growth using Google Trends,"
CREATES Research Papers
2019-13, Department of Economics and Business Economics, Aarhus University.
- 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.
- Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
Articles
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024.
"Predicting Bond Return Predictability,"
Management Science, INFORMS, vol. 70(2), pages 931-951, February.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- 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.
- 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.
- Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
- Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).
- Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
- Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
- Daniel Borup & Johan S. Jakobsen, 2019. "Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model," Quantitative Finance, Taylor & Francis Journals, vol. 19(11), pages 1839-1855, November.
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.Working papers
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022.
"The Anatomy of Out-of-Sample Forecasting Accuracy,"
FRB Atlanta Working Paper
2022-16, Federal Reserve Bank of Atlanta.
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
Cited by:
- Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
- Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
- Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
- Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020.
"Targeting predictors in random forest regression,"
CREATES Research Papers
2020-03, Department of Economics and Business Economics, Aarhus University.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
Cited by:
- Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
- Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021.
"Macroeconomic data transformations matter,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2020. "Macroeconomic Data Transformations Matter," CIRANO Working Papers 2020s-42, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Working Papers 20-17, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Mar 2021.
- Dahyun Kim & Wanhyun Cho & Inseop Na & Myung Hwan Na, 2024. "Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods," Agriculture, MDPI, vol. 14(5), pages 1-20, May.
- James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2020.
"Wild Bootstrap and Asymptotic Inference with Multiway Clustering,"
CREATES Research Papers
2020-06, Department of Economics and Business Economics, Aarhus University.
- James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2021. "Wild Bootstrap and Asymptotic Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 505-519, March.
- James G. MacKinnon & Morten Ø. Nielsen & Matthew D. Webb, 2019. "Wild Bootstrap and Asymptotic Inference with Multiway Clustering," Working Paper 1415, Economics Department, Queen's University.
- Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021.
"Active labour market policies for the long-term unemployed: New evidence from causal machine learning,"
Economics Working Paper Series
2108, University of St. Gallen, School of Economics and Political Science.
- Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning," IZA Discussion Papers 14486, Institute of Labor Economics (IZA).
- Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023.
"Machine learning advances for time series forecasting,"
Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
- 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.
- Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
- Daniel Goller & Sandro Heiniger, 2024.
"A general framework to quantify the event importance in multi-event contests,"
Annals of Operations Research, Springer, vol. 341(1), pages 71-93, October.
- Goller, Daniel & Heiniger, Sandro, 2022. "A general framework to quantify the event importance in multi-event contests," Economics Working Paper Series 2204, University of St. Gallen, School of Economics and Political Science.
- Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
- Daniel Borup & Erik Christian Montes Schütte, 2019.
"In search of a job: Forecasting employment growth using Google Trends,"
CREATES Research Papers
2019-13, Department of Economics and Business Economics, Aarhus University.
- 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.
Cited by:
- Alexander Berry & Elizabeth M. Maloney & David Neumark, 2024.
"The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies,"
NBER Working Papers
32294, National Bureau of Economic Research, Inc.
- Alexander Berry & Molly Maloney & David Neumark, 2024. "The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
- 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.
- Laurent Ferrara & Anna Simoni, 2020.
"When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage,"
EconomiX Working Papers
2020-11, University of Paris Nanterre, EconomiX.
- Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
- 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.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
- Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020.
"Targeting predictors in random forest regression,"
CREATES Research Papers
2020-03, Department of Economics and Business Economics, Aarhus University.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
- Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
- Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
- Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
- Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
- 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.
- Kerkemeier, Marco & Kruse-Becher, Robinson, 2022. "Join the club! Dynamics of global ESG indices convergence," Finance Research Letters, Elsevier, vol. 49(C).
- Daniel Borup & Martin Thyrsgaard, 2017.
"Statistical tests for equal predictive ability across multiple forecasting methods,"
CREATES Research Papers
2017-19, Department of Economics and Business Economics, Aarhus University.
Cited by:
- Michael W. McCracken, 2019.
"Diverging Tests of Equal Predictive Ability,"
Working Papers
2019-018, Federal Reserve Bank of St. Louis, revised 09 Mar 2020.
- Michael W. McCracken, 2020. "Diverging Tests of Equal Predictive Ability," Econometrica, Econometric Society, vol. 88(4), pages 1753-1754, July.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024.
"Predicting Bond Return Predictability,"
Management Science, INFORMS, vol. 70(2), pages 931-951, February.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
- Michael W. McCracken, 2019.
"Diverging Tests of Equal Predictive Ability,"
Working Papers
2019-018, Federal Reserve Bank of St. Louis, revised 09 Mar 2020.
Articles
- Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023.
"Quantifying investor narratives and their role during COVID‐19,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
Cited by:
- Walker, Clive B., 2024. "Going mainstream: Cryptocurrency narratives in newspapers," International Review of Financial Analysis, Elsevier, vol. 94(C).
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
See citations under working paper version above.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- 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.
Cited by:
- Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
- Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
- 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.
See citations under working paper version above.
- Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
- Borup, Daniel & Schütte, Erik Christian Montes, 2022.
"Asset pricing with data revisions,"
Journal of Financial Markets, Elsevier, vol. 59(PB).
Cited by:
- Chen, Zhuo & Li, Pengfei & Liao, Li & Liu, Lu & Wang, Zhengwei, 2024. "Assessing and addressing the coronavirus-induced economic crisis: Evidence from 1.5 billion sales invoices," China Economic Review, Elsevier, vol. 85(C).
- Xu, Shaojun, 2023. "Behavioral asset pricing under expected feedback mode," International Review of Financial Analysis, Elsevier, vol. 86(C).
- Borup, Daniel, 2019.
"Asset pricing model uncertainty,"
Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
Cited by:
- Horváth, Lajos & Li, Bo & Li, Hemei & Liu, Zhenya, 2020. "Time-varying beta in functional factor models: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
- Carrasco, Ignacio & Hansen, Erwin, 2022. "Asset pricing model uncertainty and portfolio choice," Finance Research Letters, Elsevier, vol. 45(C).
- Manuel Galea & David Cademartori & Roberto Curci & Alonso Molina, 2020. "Robust Inference in the Capital Asset Pricing Model Using the Multivariate t -distribution," JRFM, MDPI, vol. 13(6), pages 1-22, June.
- Daniel Borup & Johan S. Jakobsen, 2019.
"Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model,"
Quantitative Finance, Taylor & Francis Journals, vol. 19(11), pages 1839-1855, November.
Cited by:
- Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
- Heni Boubaker & Bassem Saidane & Mouna Ben Saad Zorgati, 2022. "Modelling the dynamics of stock market in the gulf cooperation council countries: evidence on persistence to shocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
- Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
- Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
- Lorraine Muguto & Paul-Francois Muzindutsi, 2022. "A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets," JRFM, MDPI, vol. 15(2), pages 1-27, February.
- Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023.
"Policy uncertainty and stock market volatility revisited: The predictive role of signal quality,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2307-2321, December.
- Afees A. Salisu & Riza Demirer & Rangan Gupta, 2022. "Policy Uncertainty and Stock Market Volatility Revisited: The Predictive Role of Signal Quality," Working Papers 202232, University of Pretoria, Department of Economics.
- Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
- Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
- Christian Conrad & Robert F. Engle, 2021. "Modelling Volatility Cycles: The (MF)2 GARCH Model," Working Paper series 21-05, Rimini Centre for Economic Analysis.
- Bahram Adrangi & Arjun Chatrath & Kambiz Raffiee, 2023. "S&P 500 volatility, volatility regimes, and economic uncertainty," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1362-1387, October.
- Lu, Xinjie & Su, Yuandong & Huang, Dengshi, 2023. "Chinese agricultural futures volatility: New insights from potential domestic and global predictors," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Wu, Xinyu & Xie, Haibin, 2021. "A realized EGARCH-MIDAS model with higher moments," Finance Research Letters, Elsevier, vol. 38(C).
- Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
- Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
- Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
- Julien Chevallier & Bilel Sanhaji, 2023.
"Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices,"
Post-Print
halshs-04344131, HAL.
- Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Stats, MDPI, vol. 6(4), pages 1-32, December.
More information
Research fields, statistics, top rankings, if available.Statistics
Access and download statistics for all items
Co-authorship network on CollEc
NEP Fields
NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 8 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.- NEP-FOR: Forecasting (6) 2019-04-08 2019-08-26 2020-04-20 2020-08-17 2022-11-28 2024-03-11. Author is listed
- NEP-BIG: Big Data (5) 2019-08-26 2020-05-25 2021-01-25 2022-11-28 2024-03-11. Author is listed
- NEP-CMP: Computational Economics (4) 2020-05-25 2021-01-25 2022-11-28 2024-03-11. Author is listed
- NEP-ECM: Econometrics (4) 2019-04-08 2020-04-20 2021-01-25 2022-11-28. Author is listed
- NEP-MAC: Macroeconomics (4) 2019-08-26 2020-05-25 2020-08-17 2021-01-25. Author is listed
- NEP-ETS: Econometric Time Series (2) 2022-11-28 2024-03-11. Author is listed
- NEP-LAB: Labour Economics (2) 2019-08-26 2021-01-25. Author is listed
- NEP-ORE: Operations Research (2) 2019-04-08 2020-08-17. Author is listed
- NEP-FMK: Financial Markets (1) 2020-08-17
- NEP-GTH: Game Theory (1) 2024-03-11
- NEP-IAS: Insurance Economics (1) 2021-01-25
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