Quantile-based GARCH-MIDAS: Estimating value-at-risk using mixed-frequency information

Yan Xu, Xinyu Wang and Hening Liu

Finance Research Letters, 2021, vol. 43, issue C

Abstract: Utilizing mixed-frequency data to predict value-at-risk of portfolio returns is promising. Inspired by the GARCH-MIDAS model (Engle et al., 2013), we propose a novel quantile-based GARCH-MIDAS model to explain how low-frequency covariates affect the quantile of high-frequency variables, being also an extension of CAViaR (Engle and Manganelli, 2004). We examine the impact of monthly economic policy uncertainty on the daily value-at-risk in the West Texas Intermediate crude oil spot and futures markets from 2000 to 2019 and find that the rise in economic policy uncertainty does drive greater WTI crude oil market risk, and vice versa.

Keywords: Quantile regression; GARCH-MIDAS; Value-at-risk forecast; Error bootstrapping method (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321000465

DOI: 10.1016/j.frl.2021.101965

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