A reality check on the GARCH-MIDAS volatility models

References

• Andersen, T. G., and T. Bollerslev. 1997. “Intraday Periodicity and Volatility Persistence in Financial Markets.” Journal of Empirical Finance 4: 115–158. doi:10.1016/S0927-5398(97)00004-2.
   Google Scholar
• Andersen, T. G., and T. Bollerslev. 1998. “Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts.” International Economic Review 39 (4): 885–905. doi:10.2307/2527343.
   Web of Science ®Google Scholar
• Asgharian, H., C. Christiansen, and A. Hou. 2016. “Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification.” Journal of Financial Econometrics 14 (3): 617–642. doi:10.1093/jjfinec/nbv025.
   Web of Science ®Google Scholar
• Asgharian, H., A. Hou, and F. Javed. 2013. “The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach.” Journal of Forecasting 32: 600–612. doi:10.1002/for.2256.
   Web of Science ®Google Scholar
• Christie, Andrew A. 1982. “The Stochastic Behavior of Common Stock Variances Value, Leverage and Interest Rate Effects.” Journal of Financial Economics 10: 407–432. doi:10.1016/0304-405X(82)90018-6.
   Web of Science ®Google Scholar
• Conrad, C., and O. Kleen. 2020. “Two are Better Than one: Volatility Forecasting Using Multiplicative Component GARCH-MIDAS Models.” Journal of Applied Econometrics 35: 19–45. doi:10.1002/jae.2742.
   Web of Science ®Google Scholar
• Conrad, C., and K. Loch. 2015. “Anticipating Long-Term Stock Market Volatility.” Journal of Applied Econometrics 30: 1090–1114. doi:10.1002/jae.2404
   Web of Science ®Google Scholar
• Conrad, C., K. Loch, and D. Rittler. 2014. “On the Macroeconomic Determinants of Long-Term Volatilities and Correlations in U.S. Stock and Crude oil Markets.” Journal of Empirical Finance 29: 26–40. doi:10.1016/j.jempfin.2014.03.009.
   Web of Science ®Google Scholar
• Cont, R. 2001. “Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues.” Quantitative Finance 1: 223–236. doi:10.1080/713665670.
   Web of Science ®Google Scholar
• Degiannakis, S., G. Filis, and R. Kizys. 2014. “The Effects of oil Price Shocks on Stock Market Volatility: Evidence from European Data.” The Energy Journal 35 (1). doi:10.5547/01956574.35.1.3.
   Web of Science ®Google Scholar
• Diebold, F. X., and R. S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13: 253–263. doi:10.1080/07350015.1995.10524599.
   Web of Science ®Google Scholar
• Eichengreen, B., and H. Tong. 2003. “Stock Market Volatility and Monetary Policy: What the Historical Record Shows.” Asset Prices and Monetary Policy, 108–142.
   Google Scholar
• Engle, R. F., E. Ghysels, and B. Sohn. 2013. “Stock Market Volatility and Macroeconomic Fundamentals.” Review of Economics and Statistics 95 (3): 776–797. doi:10.1162/REST_a_00300.
   Web of Science ®Google Scholar
• Fang, L., H. Yu, and Y. Huang. 2018. “The Role of Investor Sentiment in the Long-Term Correlation Between U.S. Stock and Bond Markets.” International Review of Economics & Finance 58: 127–139. doi:10.1016/j.iref.2018.03.005.
   Web of Science ®Google Scholar
• Ghysels, E., P. Santa-Clara, and R. Valkanov. 2004. “The MIDAS Touch: Mixed Data Sampling Regression.” Discussion Paper UNC and UCLA.
   Google Scholar
• Ghysels, E., P. Santa-Clara, and R. Valkanov. 2006. “Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies.” Journal of Econometrics 131: 59–95. doi:10.1016/j.jeconom.2005.01.004.
   Web of Science ®Google Scholar
• Ghysels, E., A. Sinko, and R. Valkanov. 2007. “MIDAS Regressions: Further Results and New Directions.” Econometric Reviews 26: 53–90. doi:10.1080/07474930600972467.
   Web of Science ®Google Scholar
• González-Rivera, G., T. H. Lee, and S. Mishra. 2004. “Forecasting Volatility: A Reality Check Based on Option Pricing, Utility Function, Value-at-Risk, and Predictive Likelihood.” International Journal of Forecasting 20 (4): 629–645. doi:10.1016/j.ijforecast.2003.10.003.
   Web of Science ®Google Scholar
• Hansen, P. R. 2005. “A Test for Superior Predictive Ability.” Journal of Business & Economic Statistics 23 (4): 365–380. doi:10.1198/073500105000000063.
   Web of Science ®Google Scholar
• Hansen, P. R., and A. Lunde. 2005. “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?” Journal of Applied Econometrics 20 (7): 873–889. doi:10.1002/jae.800.
   Web of Science ®Google Scholar
• Hansen, P. R., A. Lunde, and J. M. Nason. 2011. “The Model Confidence Set.” Econometrica 79 (2): 453–497.
   Web of Science ®Google Scholar
• Lindblad, A. 2017. “Sentiment Indicators and Macroeconomic Data as Drivers for low-Frequency Stock Market Volatility.” MPRA Paper 80266. Germany: University Library of Munich.
   Google Scholar
• Officer, R. R. 1973. “The Variability of the Market Factor of the New York Stock Exchange.” The Journal of Business 46 (3): 434–453. doi:10.1086/295551.
   Web of Science ®Google Scholar
• Pan, Z., R. Bu, and Y. Wang. 2020. “Macroeconomic Fundamentals, Jump Dynamics and Expected Volatility.” Quantitative Finance 20: 1345–1371. doi:10.1080/14697688.2020.1736317.
   Web of Science ®Google Scholar
• Pan, Z., Y. Wang, C. Wu, and Y. Libo. 2017. “Oil Price Volatility and Macroeconomic Fundamentals: A Regime Switching GARCH-MIDAS Model.” Journal of Empirical Finance 43: 130–142. doi:10.1016/j.jempfin.2017.06.005.
   Web of Science ®Google Scholar
• Politis, D. N., and J. P. Romano. 1994. “The Stationary Bootstrap.” Journal of the American Statistical Association 89: 1303–1313. doi:10.1080/01621459.1994.10476870.
   Web of Science ®Google Scholar
• Schwert, G. W. 1989. “Why Does Stock Market Volatility Change Over Time?” The Journal of Finance 44 (5): 1115–1153. doi:10.1111/j.1540-6261.1989.tb02647.x.
   Web of Science ®Google Scholar
• Shiller, Robert J. 1981a. “Alternative Tests of Rational Expectations Models.” Journal of Econometrics 16: 71–87. doi:10.1016/0304-4076(81)90076-2.
   Web of Science ®Google Scholar
• Shiller, Robert J. 1981b. “The Use of Volatility Measures in Assessing Market Efficiency.” Journal of Finance 36: 291–304. doi:10.2307/2327010.
   Web of Science ®Google Scholar
• Virk, N., and F. Javed. 2017. “European Equity Market Integration and Joint Relationship of Conditional Volatility and Correlations.” Journal of International Money and Finance 71: 53–77. doi:10.1016/j.jimonfin.2016.10.007.
   Web of Science ®Google Scholar
• West, K. 1996. “Asymptotic Inference About Predictive Ability.” Econometrica 64: 1067–1084. doi:10.2307/2171956.
   Web of Science ®Google Scholar
• White, H. 2000. “A Reality Check for Data Snooping.” Econometrica 68 (5): 1097–1126. doi:10.1111/1468-0262.00152.
   Web of Science ®Google Scholar