Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory
Carlos Martins-Filho,
Feng Yao and
Maximo Torero
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Feng Yao: West Virginia University, College of Business and Economics
No 13-05, Working Papers from Department of Economics, West Virginia University
Abstract:
We propose nonparametric estimators for conditional value-at-risk (VaR) and expected shortfall (ES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a fully nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are combined with a generalized Pareto approximation for distribution tails proposed by Pickands (1975) to give final estimators for conditional VaR and ES. We provide asymptotic characterizations of the proposed estimators and present the results of a Monte Carlo study that sheds light on their finite sample performance. Empirical viability of the model and estimators is investigated through a backtesting exercise using returns on future contracts for five agricultural commodities.
Keywords: Value-at-risk; expected shortfall; extreme value theory; nonparametric location-scale models; strong mixing (search for similar items in EconPapers)
JEL-codes: C14 C15 C22 G10 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2012-08
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Journal Article: NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY (2018) 
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Persistent link: https://EconPapers.repec.org/RePEc:wvu:wpaper:13-05
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