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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.

© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
JEL
C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models C53 - Forecasting and Prediction Methods; Simulation Methods C58 - Financial Econometrics G17 - Financial Forecasting and Simulation G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
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