Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets
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DOI: 10.1016/j.pacfin.2019.02.006
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- Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012.
"On the forecasting accuracy of multivariate GARCH models,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
- LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Sébastien Laurent & Jeroen V.K. Rombouts & Francesco Violante, 2010. "On the Forecasting Accuracy of Multivariate GARCH Models," Cahiers de recherche 1021, CIRPEE.
- 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.
- Cubadda, G. & Guardabascio, B. & Hecq, A.W., 2015. "A Vector Heterogeneous Autoregressive Index model for realized volatility measures," Research Memorandum 033, Maastricht University, Graduate School of Business and Economics (GSBE).
- Gianluca Cubadda & Barbara Guardabascio & Alain Hecq, 2016. "A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures," CEIS Research Paper 391, Tor Vergata University, CEIS, revised 23 Jul 2016.
- Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010.
"Threshold bipower variation and the impact of jumps on volatility forecasting,"
Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
- Fulvio Corsi & Davide Pirino & Roberto Reno', 2010. "Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting," LEM Papers Series 2010/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
- Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018.
"Risk Everywhere: Modeling and Managing Volatility,"
The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
- Pedersen, Lasse Heje & Bollerslev, Tim & Hood, Benjamin & Huss, John, 2018. "Risk Everywhere: Modeling and Managing Volatility," CEPR Discussion Papers 12687, C.E.P.R. Discussion Papers.
- Jan J. J. Groen & Paolo A. Pesenti, 2011.
"Commodity Prices, Commodity Currencies, and Global Economic Developments,"
NBER Chapters, in: Commodity Prices and Markets, pages 15-42,
National Bureau of Economic Research, Inc.
- Jan J. J. Groen & Paolo Pesenti, 2009. "Commodity prices, commodity currencies, and global economic developments," Staff Reports 387, Federal Reserve Bank of New York.
- Pesenti, Paolo & Groen, Jan J. J., 2010. "Commodity prices, commodity currencies, and global economic developments," CEPR Discussion Papers 7689, C.E.P.R. Discussion Papers.
- Paolo A. Pesenti & Jan J.J. Groen, 2011. "Commodity prices, commodity currencies, and global economic developments," European Economy - Economic Papers 2008 - 2015 440, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
- Jan J. J. Groen & Paolo A. Pesenti, 2010. "Commodity prices, commodity currencies, and global economic developments," NBER Working Papers 15743, National Bureau of Economic Research, Inc.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007.
"Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility,"
The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, Department of Economics and Business Economics, Aarhus University.
- Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
- Becker, Ralf & Clements, Adam E., 2008.
"Are combination forecasts of S&P 500 volatility statistically superior?,"
International Journal of Forecasting, Elsevier, vol. 24(1), pages 122-133.
- Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
- Malcolm Baker & Jeffrey Wurgler, 2006.
"Investor Sentiment and the Cross‐Section of Stock Returns,"
Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
- Malcolm Baker & Jeffrey Wurgler, 2004. "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, National Bureau of Economic Research, Inc.
- Degiannakis, Stavros & Filis, George, 2017.
"Forecasting oil price realized volatility using information channels from other asset classes,"
Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
- Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
- Andrew J. Patton & Tarun Ramadorai, 2013.
"On the High-Frequency Dynamics of Hedge Fund Risk Exposures,"
Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
- Patton, Andrew, 2011. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," CEPR Discussion Papers 8479, C.E.P.R. Discussion Papers.
- Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008.
"Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise,"
Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
- Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
- Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Papers 2006-W03, Economics Group, Nuffield College, University of Oxford.
- Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
- Malcolm Baker & Jeffrey Wurgler, 2007.
"Investor Sentiment in the Stock Market,"
Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
- Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," NBER Working Papers 13189, National Bureau of Economic Research, Inc.
- Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
- Ludvigson, Sydney C. & Ng, Serena, 2007.
"The empirical risk-return relation: A factor analysis approach,"
Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
- Sydney C. Ludvigson & Serena Ng, 2005. "The Empirical Risk-Return Relation: A Factor Analysis Approach," NBER Working Papers 11477, National Bureau of Economic Research, Inc.
- Sydney Ludvigson & Serena Ng, 2006. "The Empirical Risk-Return Relation: a factor analysis approach," 2006 Meeting Papers 236, Society for Economic Dynamics.
- Alessandra Amendola & Giuseppe Storti, 2015. "Model Uncertainty and Forecast Combination in High‐Dimensional Multivariate Volatility Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 83-91, March.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
- Timmermann, Allan, 2006.
"Forecast Combinations,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196,
Elsevier.
- Timmermann, Allan, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
- Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
- Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, Department of Economics and Business Economics, Aarhus University.
- Bekaert, Geert & Hoerova, Marie, 2014.
"The VIX, the variance premium and stock market volatility,"
Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
- Geert Bekaert & Marie Hoerova, 2013. "The VIX, the Variance Premium and Stock Market Volatility," NBER Working Papers 18995, National Bureau of Economic Research, Inc.
- Hoerova, Marie & Bekaert, Geert, 2014. "The VIX, the variance premium and stock market volatility," Working Paper Series 1675, European Central Bank.
- Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011.
"The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets,"
Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
- Thomas Busch & Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets," CREATES Research Papers 2007-09, Department of Economics and Business Economics, Aarhus University.
- Bent Jesper Christensen & Morten Ø. Nielsen & Thomas Busch, 2008. "The Role Of Implied Volatility In Forecasting Future Realized Volatility And Jumps In Foreign Exchange, Stock, And Bond Markets," Working Paper 1181, Economics Department, Queen's University.
- Capistrán, Carlos & Timmermann, Allan, 2009.
"Forecast Combination With Entry and Exit of Experts,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
- Timmermann Allan & Capistrán Carlos, 2006. "Forecast Combination with Entry and Exit of Experts," Working Papers 2006-08, Banco de México.
- Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
- Awartani, Basel & Corradi, Valentina & Distaso, Walter, 2009. "Assessing Market Microstructure Effects via Realized Volatility Measures with an Application to the Dow Jones Industrial Average Stocks," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 251-265.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014.
"Forecasting the Equity Risk Premium: The Role of Technical Indicators,"
Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
- Pierre Giot & Sébastien Laurent, 2007. "The information content of implied volatility in light of the jump/continuous decomposition of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(4), pages 337-359, April.
- Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2016. "Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1127-1163, December.
- F. M. Bandi & J. R. Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(2), pages 339-369.
- Barbara Rossi & Atsushi Inoue, 2012.
"Out-of-Sample Forecast Tests Robust to the Choice of Window Size,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
- Rossi, Barbara & Inoue, Atsushi, 2011. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers.
- Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
- Barbara Rossi & Atsushi Inoue, 2012. "Out-of-sample forecast tests robust to the choice of window size," Economics Working Papers 1404, Department of Economics and Business, Universitat Pompeu Fabra.
- Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
- Sydney C. Ludvigson & Serena Ng, 2009.
"Macro Factors in Bond Risk Premia,"
The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
- Sydeny C. Ludvigson & Serena Ng, 2005. "Macro Factors in Bond Risk Premia," NBER Working Papers 11703, National Bureau of Economic Research, Inc.
- Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2008.
"Quantile forecasts of daily exchange rate returns from forecasts of realized volatility,"
Journal of Empirical Finance, Elsevier, vol. 15(4), pages 729-750, September.
- Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
- Clements, Michael P. & Galvao, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," Economic Research Papers 269747, University of Warwick - Department of Economics.
- Kaminska, Iryna & Roberts-Sklar, Matt, 2018.
"Volatility in equity markets and monetary policy rate uncertainty,"
Journal of Empirical Finance, Elsevier, vol. 45(C), pages 68-83.
- Kaminska, Iryna & Roberts-Sklar, Matt, 2017. "Volatility in equity markets and monetary policy rate uncertainty," Bank of England working papers 700, Bank of England.
- Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
- Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015.
"Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes,"
Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
- Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
- Nathaniel Light & Denys Maslov & Oleg Rytchkov, 2017. "Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1339-1381.
- Patton, Andrew J., 2011.
"Volatility forecast comparison using imperfect volatility proxies,"
Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
- Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
- O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
- Pesaran, M. Hashem & Timmermann, Allan, 2009.
"Testing Dependence Among Serially Correlated Multicategory Variables,"
Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
- M. Hashem Pesaran & Allan Timmermann, 2006. "Testing Dependence among Serially Correlated Multi-category Variables," CESifo Working Paper Series 1770, CESifo.
- Pesaran, M.H. & Timmermann, A., 2006. "Testing Dependence Among Serially Correlated Multi-category Variables," Cambridge Working Papers in Economics 0648, Faculty of Economics, University of Cambridge.
- Pesaran, M. Hashem & Timmermann, Allan, 2006. "Testing Dependence among Serially Correlated Multi-Category Variables," IZA Discussion Papers 2196, Institute of Labor Economics (IZA).
- Chun Liu & John M. Maheu, 2009.
"Forecasting realized volatility: a Bayesian model-averaging approach,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
- Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
- Clements, Adam & Liao, Yin, 2017. "Forecasting the variance of stock index returns using jumps and cojumps," International Journal of Forecasting, Elsevier, vol. 33(3), pages 729-742.
- David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
- Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005.
"Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements,"
Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
- Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.
- Eugenie Hol & Siem Jan Koopman & Borus Jungbacker, 2004. "Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements," Computing in Economics and Finance 2004 342, Society for Computational Economics.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
- Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016.
"Exploiting the errors: A simple approach for improved volatility forecasting,"
Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
- Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2015. "Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting," CREATES Research Papers 2015-14, Department of Economics and Business Economics, Aarhus University.
- Lin, Qi, 2018. "Technical analysis and stock return predictability: An aligned approach," Journal of Financial Markets, Elsevier, vol. 38(C), pages 103-123.
- Neil Shephard & Kevin Sheppard, 2010.
"Realising the future: forecasting with high-frequency-based volatility (HEAVY) models,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," OFRC Working Papers Series 2009fe02, Oxford Financial Research Centre.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Series Working Papers 438, University of Oxford, Department of Economics.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Papers 2009-W03, Economics Group, Nuffield College, University of Oxford.
- Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
- Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
- Gao, Lin & Süss, Stephan, 2015. "Market sentiment in commodity futures returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 84-103.
- Ductor, Lorenzo & Leiva-Leon, Danilo, 2016.
"Dynamics of global business cycle interdependence,"
Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.
- Lorenzo Ductor & Danilo Leiva-Leon, 2015. "Dynamics of Global Business Cycles Interdependence," Working Papers Central Bank of Chile 763, Central Bank of Chile.
- Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015.
"Investor Sentiment Aligned: A Powerful Predictor of Stock Returns,"
The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
- Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," CEMA Working Papers 676, China Economics and Management Academy, Central University of Finance and Economics.
- Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016.
"Modeling and forecasting exchange rate volatility in time-frequency domain,"
European Journal of Operational Research, Elsevier, vol. 251(1), pages 329-340.
- Jozef Barunik & Tomas Krehlik & Lukas Vacha, 2012. "Modeling and forecasting exchange rate volatility in time-frequency domain," Papers 1204.1452, arXiv.org, revised Feb 2015.
- Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain," FinMaP-Working Papers 55, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
- repec:hal:journl:peer-00741630 is not listed on IDEAS
- repec:wyi:journl:002169 is not listed on IDEAS
- Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
- Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
- Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2015. "Intra-daily volatility spillovers in international stock markets," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 95-114.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006.
"Predicting volatility: getting the most out of return data sampled at different frequencies,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Ole E. Barndorff-Nielsen, 2004.
"Power and Bipower Variation with Stochastic Volatility and Jumps,"
Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Power and bipower variation with stochastic volatility and jumps," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
- Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
- Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2017. "A general framework for discretely sampled realized variance derivatives in stochastic volatility models with jumps," European Journal of Operational Research, Elsevier, vol. 262(1), pages 381-400.
- Buncic, Daniel & Gisler, Katja I.M., 2017. "The role of jumps and leverage in forecasting volatility in international equity markets," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 1-19.
- Sévi, Benoît, 2014.
"Forecasting the volatility of crude oil futures using intraday data,"
European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
- Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Post-Print hal-01463921, HAL.
- Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Working Papers 2014-53, Department of Research, Ipag Business School.
- Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
- Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
- Duong, Diep & Swanson, Norman R., 2015.
"Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction,"
Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
- Diep Duong & Norman Swanson, 2013. "Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction," Departmental Working Papers 201321, Rutgers University, Department of Economics.
- Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
- Feng Ma & Yu Wei & Li Liu & Dengshi Huang, 2018. "Forecasting realized volatility of oil futures market: A new insight," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(4), pages 419-436, July.
- Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
- Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
- Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
- Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
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More about this item
Keywords
Volatility forecasting; G7 stock markets; Realized volatility; Jumps; Partial least squares;All these keywords.
JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
Statistics
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