Volatility specifications versus probability distributions in VaR forecasting

Laura Garcia‐Jorcano and Alfonso Novales

Journal of Forecasting, 2021, vol. 40, issue 2, 189-212

Abstract: We provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for value‐at‐risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR forecasting, and they should be preferred when estimating tail risk. The flexibility of the free power parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates suggest that for a number of financial assets the dynamics of volatility should be specified in terms of the conditional standard deviation. We draw our results on VaR forecasting performance from (i) a variety of backtesting approaches, (ii) the model confidence set approach, as well as (iii) establishing a ranking among alternative VaR models using a precedence criterion that we introduce in this paper.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1002/for.2697

Related works:
Working Paper: Volatility specifications versus probability distributions in VaR forecasting (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:2:p:189-212

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().