Factor High-Frequency-Based Volatility (HEAVY) Models

Kevin Sheppard and Wen Xu

Journal of Financial Econometrics, 2019, vol. 17, issue 1, 33-65

Abstract: We propose a new class of multivariate volatility models utilizing realized measures of asset variance and covariance 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 stationarity of returns, are established. The performance of the model is assessed using a panel 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, covariances, betas, and scenario-based risk measures. The model’s performance is particularly strong at short forecast horizons.

Keywords: conditional beta; conditional covariance; forecasting; realized covariance; realized Kernel; systematic risk (search for similar items in EconPapers)
JEL-codes: C32 C53 C58 G17 G21 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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