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Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
Vincenzo Candila1, Giampiero M Gallo2, Lea Petrella3
1Department of Economics and Statistics, University of Salerno, Fisciano, Italy.
Annals of Operations Research
|June 26, 2023
Summary
This study introduces a novel mixed-frequency quantile regression model to estimate financial risk measures like Value-at-Risk (VaR) and Expected Shortfall (ES). The model effectively integrates low and high-frequency data for improved risk assessment.
Area of Science:
- Quantitative Finance
- Econometrics
Background:
- Quantile regression is standard for financial risk measures.
- Existing methods face challenges with mixed-frequency data.
Purpose of the Study:
- Develop a mixed-frequency quantile regression model.
- Directly estimate Value-at-Risk (VaR) and Expected Shortfall (ES).
Main Methods:
- Incorporating low-frequency (e.g., monthly) and high-frequency (e.g., daily) variables.
- Deriving weak stationarity conditions for daily returns.
- Conducting extensive Monte Carlo simulations.
Main Results:
- The proposed model effectively estimates VaR and ES using mixed-frequency data.
- Demonstrated superior performance against competing models in backtesting.
- Validated through an empirical application on energy commodities (Crude Oil, Gasoline futures).
Conclusions:
- The novel mixed-frequency quantile regression offers enhanced risk measurement capabilities.
- Outperforms existing methods, particularly for energy commodity markets.
- Provides a robust framework for financial risk analysis with mixed-frequency data.
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