Template-type: ReDIF-Paper 1.0
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.
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.
Title: Backtesting Extreme Value Theory models of expected shortfall
Abstract: We use stock market data to analyze the quality of alternative models and procedures for fore- casting expected shortfall (ES) at different significance levels. We 
	compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory 
	(EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day hori- zon we also 
	combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest 
	that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability 
	distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting 
	performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the 
	range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES 
	forecasts.
Keywords: Extreme value theory; Skewed distributions; Expected shortfall; Backtesting; Filtered historical simulation.
Length: 51 pages 
Creation-Date: 2019-09
Number: 2019-24
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1924.txt
File-URL: https://eprints.ucm.es/id/eprint/57130/1/1924.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1924