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Comparison of semiparametric methods to estimate VaR and ES

조건부 Value-at-Risk와 Expected Shortfall 추정을 위한 준모수적 방법들의 비교 연구

  • Received : 2015.12.15
  • Accepted : 2015.12.23
  • Published : 2016.02.29

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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Cited by

  1. On Entropy Test for Conditionally Heteroscedastic Location-Scale Time Series Models vol.19, pp.8, 2017, https://doi.org/10.3390/e19080388
  2. Bootstrap entropy test for general location-scale time series models with heteroscedasticity vol.88, pp.13, 2018, https://doi.org/10.1080/00949655.2018.1478977

Acknowledgement

Supported by : 한국연구재단