Template-type: ReDIF-Paper 1.0
Author-Name: Laura Garcia-Jorcano
Author-Workplace-Name: Department of Economic Analysis and Finance (Area of Financial Economics), Facultad de Ciencias Jurídicas y Sociales, Universidad de Castilla-La Mancha,
	Toledo, Spain.
Author-Name: Alfonso Novales
Author-Workplace-Name: Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad 
	Complutense, 28223 Madrid, Spain.
Title: Volatility specifications versus probability distributions in VaR forecasting
Abstract: We provide evidence suggesting that the assumption on the probability distribution for return in- novations 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 fore- casting, 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 Confi- dence Set approach, as well as iii) establishing a ranking among alternative VaR models using a precedence 
	criterion that we introduce in this paper.
Keywords: Value-at-risk; Backtesting; Evaluating forecasts; Precedence; APARCH model; Asym- metric distributions.
Length: 39 pages 
Creation-Date: 2019-09
Number: 2019-26
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1926.txt
File-URL: https://eprints.ucm.es/id/eprint/57135/1/1926.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1926