Does Decomposing Realized Volatility Help in Risk Prediction: Evidence from Chinese Mainland Stocks
Australian National University (ANU); Financial Research Network (FIRN)
January 13, 2011
This article studies the risk forecasting properties of three realized volatility models for three Chinese individual stocks, and reveals the important role that jumps can play in risk prediction. I firstly investigate dynamic pattern of jumps in three Chinese stocks, and find that relative to developed markets, jumps in this emerging market are more predictable and account for a larger proportion in realized volatility. Further, I compare the Value-at-risk (VaR) forecasting performances of three commonly used realized volatility models for the three Chinese stocks. Two-step VaR backtesting shows that a newly proposed realized volatility forecasting model (Andersen et al, 2007), which separately treats jumps and the continuous sample path of the asset price, provides more accurate VaR prediction than two other competing models, that treat realized volatility as a single variable. These findings suggest that carefully modelling of jumps is important in risk prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.
Number of Pages in PDF File: 31
Keywords: Value-at-Risk, Realized volatility, Bi-power variation, Jumps, HAR-CJN model
JEL Classification: C13, C32, C52, C53, G17, G32
Date posted: February 14, 2011
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