Modeling Realized Volatility Dynamics with a Genetic Algorithm

Modeling Realized Volatility Dynamics with a Genetic Algorithm

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Article ID: iaor20162635
Volume: 35
Issue: 5
Start Page Number: 434
End Page Number: 444
Publication Date: Aug 2016
Journal: Journal of Forecasting
Authors: ,
Keywords: time series: forecasting methods, simulation, statistics: regression, heuristics: genetic algorithms
Abstract:

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.

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