A Parsimonious Quantile Regression Model to Forecast Day-Ahead Value-at-risk
Finance research letters(2016)
Lillehammer Univ Coll | Univ Colorado | James Madison Univ | The Norwegian University of Science and Technology
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. (C) 2015 Elsevier Inc. All rights reserved.
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Key words
Heterogeneous investors,HAR-QREG/Quantile regression,Risk management,Value-at-risk,Volatility
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