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http://dx.doi.org/10.5351/KJAS.2016.29.1.171

Comparison of semiparametric methods to estimate VaR and ES  

Kim, Minjo (Department of Statistics, Seoul National University)
Lee, Sangyeol (Department of Statistics, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.1, 2016 , pp. 171-180 More about this Journal
Abstract
Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.
Keywords
Value-at-Risk; expected shortfall; CAViaR method; CARE method; Gaussian QMLE; back-testing method;
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Times Cited By KSCI : 1  (Citation Analysis)
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