Evaluating Combined Forecasts for Realized Volatility Using Asymmetric Loss Functions
Abstract
In this work we provide the findings of a forecast combination analysis carried out on the realized volatility series of three market indexes (DAX, CAC, and AEX). Two volatility types (5 minutes, kernel) have been considered. Different loss functions suggest that forecasts computed through combining models are generally more accurate than those provided by single models. However, the choice of the latter can significantly affect the goodness of the results.
References
Ane, T. (2006). An analysis of the flexibility of asymmetric power GARCH models. Computational Statistics and Data Analysis, 51(2):1293–1311.
Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3):339–350.
Engle, R. and Gallo, G. (2006). A multiple indicators model for volatility using intra-daily data. Journal of Econometrics, 131(1):3–27.
Fernandes, M. and Grammig, J. (2006). A family of autoregressive conditional duration models. Journal of Econometrics, 130(1):1–23.
Giacomini, R. and White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6):1545–1578.
Oxford-Man Instituten of Quantitative Finance (2017). Realized Library. http://realized.oxford-man.ox.ac.uk/data.
Patton, A. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1):246–256.
Raviv, E. (2016). Forecast combinations in R using the ForecastCombinations package. A Manual.
Timmermann, A. (2006). Forecast combinations. Handbook of Economic Forecasting, 1:135–196.
Copyright (c) 2017 by the Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
