A parsimonious quantile regression model to forecast day-ahead value-at-risk

Erik Haugom, Rina Ray, Carl J. Ullrich, Steinar Veka and Sjur Westgaard

Finance Research Letters, 2016, vol. 16, issue C, 196-207

Abstract: This paper proposes a parsimonious quantile regression model for forecasting Value-at-Risk. The model uses only observable measures of daily, weekly, and monthly volatility as input and thus simplifies optimization substantially compared with other methods proposed in the literature. The framework also provides a new way of illustrating the volatility effects of a heterogeneous market. When subjected to formal coverage tests for out-of-sample VaR predictions, model performance is similar to more complicated models.

Keywords: Heterogeneous investors; HAR-QREG/Quantile regression; Risk management; Value-at-risk; Volatility (search for similar items in EconPapers)
JEL-codes: G15 G17 G28 G29 G32 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:16:y:2016:i:c:p:196-207

DOI: 10.1016/j.frl.2015.12.006

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