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Forecasting value at risk and expected shortfall with mixed data sampling |
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| Institution: | 1. School of Economics & Management, Southwest Jiaotong University, Chengdu, China;2. School of Marxism, Yunnan University of Finance and Economics, Kunming, China;3. School of Econimcs, Yunnan University of Finance and Economics, Kunming, China;4. School of Finance, Yunnan University of Finance and Economics, 237 Longquan Road, Kunming, Yunnan, China;1. ESSEC Business School, CREAR risk research center, France;2. Heriot Watt University, United Kingdom;3. University of York, The York Management School, United Kingdom |
| 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 on short-horizon returns to directly forecast the tail dynamics of 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 in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES. |
| Keywords: | Mixed Data Sampling (MIDAS) Value at risk Expected shortfall Backtests Model confidence set |
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