Multiple-step value-at-risk forecasts based on volatility-filtered MIDAS quantile regression: Evidence from major investment assets
Open Access
- 20 September 2021
- journal article
- Published by LLC CPC Business Perspectives in Investment Management and Financial Innovations
- Vol. 18 (3), 372-384
- https://doi.org/10.21511/imfi.18(3).2021.31
Abstract
Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.Keywords
This publication has 42 references indexed in Scilit:
- A comprehensive review of Value at Risk methodologiesThe Spanish Review of Financial Economics, 2014
- Caviar and the Australian Stock Markets: An AppetiserSSRN Electronic Journal, 2010
- Predicting volatility: getting the most out of return data sampled at different frequenciesJournal of Econometrics, 2006
- CAViaRJournal of Business & Economic Statistics, 2004
- Value‐at‐risk for long and short trading positionsJournal of Applied Econometrics, 2003
- Evaluating Interval ForecastsInternational Economic Review, 1998
- Regulatory Evaluation of Value-at-Risk ModelSSRN Electronic Journal, 1997
- An interior point algorithm for nonlinear quantile regressionJournal of Econometrics, 1996
- Good News, Bad News, Volatility, and BetasThe Journal of Finance, 1995
- Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 1986