Jump Robust Two Time Scale Covariance Estimation and Realized Volatility Budgets

Quantitative Finance, Vol. 15, No. 6, 1041-1054

30 Pages Posted: 1 Nov 2010 Last revised: 8 Nov 2017

See all articles by Kris Boudt

Kris Boudt

Ghent University; Vrije Universiteit Brussel; Vrije Universiteit Amsterdam

Jin Zhang

Bank of America

Date Written: October 29, 2010


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

Suggested Citation

Boudt, Kris and Zhang, Jin, Jump Robust Two Time Scale Covariance Estimation and Realized Volatility Budgets (October 29, 2010). Quantitative Finance, Vol. 15, No. 6, 1041-1054, Available at SSRN: https://ssrn.com/abstract=1699730 or http://dx.doi.org/10.2139/ssrn.1699730

Kris Boudt (Contact Author)

Ghent University ( email )

Sint-Pietersplein 5
Gent, 9000

Vrije Universiteit Brussel ( email )

Pleinlaan 2
Brussels, 1050

Vrije Universiteit Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV

Jin Zhang

Bank of America ( email )

NEW YORK, NY 10281
United States

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