Outlier-robust methods for forecasting realized covariance matrices

Dan Li, Christopher Drovandi and Adam Clements

International Journal of Forecasting, 2024, vol. 40, issue 1, 392-408

Abstract: This paper proposes two new approaches to improve the estimation of the coefficients of the multivariate HAR (MHAR) model with the primary purpose of improving forecast performance. A robust estimator of the covariance matrix is adopted to replace the realized covariance matrix while estimating the MHAR model. The robustness to outliers of the new estimator makes the OLS estimation scheme for the MHAR model more reliable. In addition, a robust estimation scheme is developed for the MHAR model, which is based on the multivariate least-trimmed squares method. Both approaches provide significant improvements in forecasting performance based on both statistical loss and portfolio outcomes. The forecast performance of the multivariate HARQ model can also be improved with the proposed approaches, as evidenced by robustness checks.

Keywords: Multivariate volatility; HAR; Portfolio allocation; Minimum covariance determinant; Multivariate regression; Least-trimmed squares estimator (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:1:p:392-408

DOI: 10.1016/j.ijforecast.2023.04.004

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