Quantile forecast combinations in realised volatility prediction

References

• Amisano, G., & Giacomini, R. (2007). Comparing density forecasts via weighted likelihood ratio tests. Journal of Business and Economic Statistics, 25(2), 177–190.
   Web of Science ®Google Scholar
• Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579–626.
   Web of Science ®Google Scholar
• Andersen, T. G., Bollerslev T., Christoffersen, P., & Diebold, F. X. (2006). Volatility and correlation forecasting. In G. Elliot, C. W. Granger, & A. Timmermann (eds.), Handbook of economic forecasting (pp. 777–878). Amsterdam: Elsevier.
   Google Scholar
• Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20(4), 451–468.
   Google Scholar
• Chernozhukov, C., Fernandez-Val, I., & Galichon, A. (2010). Quantile and probability curves without crossing. Econometrica, 78, 1093–1125.
   Web of Science ®Google Scholar
• Christiansen, C., Schmeling, M., & Schrimpf, A. (2012). A comprehensive look at financial volatility prediction by economic variables. Journal of Applied Econometrics, 27(6), 956–977.
   Web of Science ®Google Scholar
• Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics, 138(1), 291–311.
   Web of Science ®Google Scholar
• Conrad, C., & Loch, K. (2015). Anticipating long-term stock market volatility. Journal of Applied Econometrics, 30(7), 1090–1114.
   Web of Science ®Google Scholar
• Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253–263.
   Web of Science ®Google Scholar
• Elliott, G., Gargano, A., & Timmermann, A. (2013). Complete subset regressions. Journal of Econometrics, 177(2), 357–373.
   Web of Science ®Google Scholar
• Elliott, G., Gargano, A., & Timmermann, A. (2015). Complete subset regressions with large-dimensional sets of predictors. Journal of Economic Dynamics and Control, 54, 86–110.
   Web of Science ®Google Scholar
• Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
   Web of Science ®Google Scholar
• Gastwirth, J. L. (1966). On robust procedures. Journal of the American Statistical Association, 61(316), 929–948.
   Web of Science ®Google Scholar
• Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.
   Web of Science ®Google Scholar
• Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801.
   Web of Science ®Google Scholar
• Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359–378.
   Web of Science ®Google Scholar
• Gneiting, T., & Ranjan, R. (2011). Comparing density forecasts using threshold and quantile weighted scoring rules. Journal of Business and Economic Statistics, 29(3), 411–422.
   Web of Science ®Google Scholar
• Goyal, A., & Welch, I. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455–1508.
   Web of Science ®Google Scholar
• Huang, H., & Lee, T. H. (2010). To combine forecasts or to combine information. Econometric Reviews, 29(5–6), 534–571.
   Web of Science ®Google Scholar
• Judge, G. G., Hill, R. C., Griffiths, W. E., Lutkepohl, H., & Lee, T.-C. (1988). Introduction to the theory and practice of econometrics. New York, NY: Wiley.
   Google Scholar
• Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
   Web of Science ®Google Scholar
• Koenker, R., & Xiao, Z. (2006). Quantile autoregressions. Journal of the American Statistical Association, 101(475), 980–990.
   Web of Science ®Google Scholar
• Manzan, S. (2015). Forecasting the distribution of economic variables in a data-rich environment. Journal of Business and Economic Statistics, 33(1), 144–164.
   Web of Science ®Google Scholar
• Manzan, S., & Zerom, D. (2013). Are macroeconomic variables useful for forecasting U.S. inflation? International Journal of Forecasting, 29(3), 469–478.
   Web of Science ®Google Scholar
• Meligkotsidou, L., Panopoulou, E., Vrontos, I. D., & Vrontos, S. D. (2014a). A quantile regression approach to equity premium prediction. Journal of Forecasting, 33(7), 558–576.
   Web of Science ®Google Scholar
• Meligkotsidou, L., Panopoulou, E., Vrontos, I. D., & Vrontos, S. D. (2014b). Out-of-sample equity premium prediction: A complete subset quantile regression approach. Kent Business School working paper, University of Kent, Canterbury, UK. available at https://kar.kent.ac.uk/45149/.
   Google Scholar
• Mittnik, S., Robinzonov, N., & Spindler, M. (2015). Stock market volatility: Identifying major drivers and the nature of their impact. Journal of Banking and Finance, 58, 1–14.
   Web of Science ®Google Scholar
• Panopoulou, E., & Vrontos, S. (2015). Hedge fund return predictability; To combine forecasts or combine information? Journal of Banking and Finance, 56, 103–122.
   Web of Science ®Google Scholar
• Paye, B. S. (2012). ‘Deja Vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106(l), 527–546.
   Web of Science ®Google Scholar
• Pedersen, T. Q. (2015). Predictable return distributions. Journal of Forecasting, 34(2), 114–132.
   Web of Science ®Google Scholar
• Rapach, D., Strauss, J., & Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), 821–862.
   Web of Science ®Google Scholar
• Schwert, G. (1989). Why does stock market volatility change over time? Journal of Finance, 44(5), 1115–1153.
   Web of Science ®Google Scholar
• Shan, K., & Yang, Y. (2009). Combining regression quantile estimators. Statistica Sinica, 19, 1171–1191.
   Web of Science ®Google Scholar
• Timmermann, A. (2006). Forecast combinations. In G. Elliott, C. W. J. Granger, & A. Timmermann (eds.), Handbook of economic forecasting (Vol. I). Amsterdam: Elsevier.
   Google Scholar
• Tsiotas, G. (2015). A quasi-Bayesian model averaging approach for conditional quantile models. Journal of Statistical Computation and Simulation, 85(10), 1963–1986.
   Web of Science ®Google Scholar
• Tukey, J. W. (1977). Explanatory data analysis. Reading, MA: Addison-Wesley.
   Google Scholar
• West, K. D. (1996). Asymptotic inference about predictive ability. Econometrica, 64(5), 1067–1084.
   Web of Science ®Google Scholar
• Yu, K., & Zhang, J. (2005). A three-parameter asymmetric Laplace distribution and its extension. Communications in Statistics - Theory and Methods, 34(9–10), 1867–1879.
   Web of Science ®Google Scholar
• Yu, K., & Moyeed, R. A. (2001). Bayesian quantile regression. Statistics and Probability Letters, 54(4), 437–447.
   Web of Science ®Google Scholar