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Model averaging estimation for conditional volatility models with an application to stock market volatility forecast
Author(s) -
Liu Qingfeng,
Yao Qingsong,
Zhao Guoqing
Publication year - 2020
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2659
Subject(s) - econometrics , volatility (finance) , estimator , stochastic volatility , conditional variance , forward volatility , stock market , mathematics , economics , statistics , autoregressive conditional heteroskedasticity , paleontology , horse , biology
Abstract This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight‐choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1‐day‐ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.