Forecasting Value at Risk and Expected Shortfall with Mixed Data Sampling

55 Pages Posted: 15 Jan 2020 Last revised: 17 Apr 2020

See all articles by Trung H. Le

Trung H. Le

State Bank of Vietnam - Banking Academy of Vietnam

Date Written: December 25, 2019

Abstract

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence in short-horizon returns to directly forecast the tail dynamics at the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially at multi-day forecast horizons. My analysis advocates models featuring asymmetric conditional quantile and the use of Asymmetric Laplace density to jointly estimate VaR and ES.

Keywords: Value at Risk, Expected Shortfall, Mixed Data Sampling, Model Confidence Set, Backtesting

JEL Classification: G170, C180, C140

Suggested Citation

Le, Trung H., Forecasting Value at Risk and Expected Shortfall with Mixed Data Sampling (December 25, 2019). International Journal of Forecasting, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3509425 or http://dx.doi.org/10.2139/ssrn.3509425

Trung H. Le (Contact Author)

State Bank of Vietnam - Banking Academy of Vietnam ( email )

No.12, Chuaboc Street
Dong Da District
Hanoi, Hanoi 10000
Vietnam

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