Forecasting Realized Covariances Using HAR-Type Models

Matias Quiroz (), Laleh Tafakori () and Hans Manner ()
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Matias Quiroz: University of Technology Sydney, Australia
Laleh Tafakori: RMIT University, Australia
Hans Manner: University of Graz, Austria

No 2024-20, Graz Economics Papers from University of Graz, Department of Economics

Abstract: We investigate methods for forecasting multivariate realized covariances matrices applied to a set of 30 assets that were included in the DJ30 index at some point, including two novel methods that use existing (univariate) log of realized variance models that account for attenuation bias and time-varying parameters. We consider the implications of some modeling choices within the class of heterogeneous autoregressive models. The following are our key findings. First, modeling the logs of the marginal volatilities is strongly preferred over direct modeling of marginal volatility. Thus, our proposed model that accounts for attenuation bias (for the log-response) provides superior one-step-ahead forecasts over existing multivariate realized covariance approaches. Second, accounting for measurement errors in marginal realized variances generally improves multivariate forecasting performance, but to a lesser degree than previously found in the literature. Third, time-varying parameter models based on state-space models perform almost equally well. Fourth, statistical and economic criteria for comparing the forecasting performance lead to some differences in the model's rankings, which can partially be explained by the turbulent post-pandemic data in our out-of-sample validation dataset using sub-sample analyses.

Keywords: State space model; Heterogeneous autoregressive; Realized measures; Volatility forecasting. (search for similar items in EconPapers)
JEL-codes: C51 C53 G17 (search for similar items in EconPapers)
Date: 2024-12
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