Factor High-Frequency Based Volatility (HEAVY) Models

47 Pages Posted: 28 May 2014

See all articles by Kevin Sheppard

Kevin Sheppard

University of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance

Wen Xu

Capital University of Economics and Business-International School of Economics and Management

Date Written: May 27, 2014

Abstract

We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationary or returns, are established. The model is applied to modeling the conditional covariance data of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covolatilities, betas and scenario-based risk measures, where the model's performance is particularly strong at short forecast horizons.

Keywords: Conditional Beta, Conditional Covariance, Forecasting, HEAVY, Marginal Expected Shortfall, Realized Covariance, Realized Kernel, Systematic Risk

JEL Classification: C32, C53, C58, G17, G21

Suggested Citation

Sheppard, Kevin Keith and Xu, Wen, Factor High-Frequency Based Volatility (HEAVY) Models (May 27, 2014). Available at SSRN: https://ssrn.com/abstract=2442230 or http://dx.doi.org/10.2139/ssrn.2442230

Kevin Keith Sheppard (Contact Author)

University of Oxford - Department of Economics ( email )

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Oxford, OX1 3BJ
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University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

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HOME PAGE: http://www.oxford-man.ox.ac.uk

Wen Xu

Capital University of Economics and Business-International School of Economics and Management

Beijing
China

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