Jump Robust Two Time Scale Covariance Estimation and Realized Volatility Budgets
Jin Zhang
Stuart School of Business
IIT
We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean square error in simulation studies. We use these estimators to construct the ex-post portfolio realized volatility (RV) budget, determining each portfolio component’s contribution to the RV of the portfolio return. These RV budgets provide insight into the risk concentration of a portfolio. Furthermore, the RV budgets can be directly used in a portfolio strategy, called the equal-risk-contribution allocation strategy. This yields both a higher average return and lower standard deviation out-of-sample than the equal-weight portfolio for the stocks in the Dow Jones Industrial Average over the period October 2007-May 2009.
KEYWORDS: High frequency data, Integrated (co)variance, Jumps, Market microstructure noise, Realized volatility budget.
JEL classification: C13, C15, G11.
(This is joint work with Kris Boudt.)
