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Modeling Realized Volatility Dynamics with a Genetic Algorithm
Author(s) -
Qu Hui,
Ji Ping
Publication year - 2016
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.2386
Subject(s) - volatility (finance) , autoregressive model , econometrics , stock market , lag , realized variance , computer science , economics , paleontology , computer network , horse , biology
The heterogeneous autoregressive model of realized volatility (HAR‐RV) is inspired by the heterogeneous market hypothesis and characterizes realized volatility dynamics through a linear function of lagged daily, weekly and monthly realized volatilities with a (1, 5, 22) lag structure. Considering that different markets can have different heterogeneous structures and a market's heterogeneous structure can vary over time, we build an adaptive heterogeneous autoregressive model of realized volatility (AHAR‐RV), whose lag structure is optimized with a genetic algorithm. Using nine common loss functions and the superior predictive ability test, we find that our AHAR‐RV model and its extensions provide significantly better out‐of‐sample volatility forecasts for the CSI 300 index than the corresponding HAR models. Furthermore, the AHAR‐RV model significantly outperforms all the other models under most loss functions. Besides, we confirm that Chinese stock markets' heterogeneous structure varies over time and the (1, 5, 22) lag structure is not the optimal choice. Copyright © 2016 John Wiley & Sons, Ltd.