Dependent bootstrapping for value-at-risk and expected shortfall
Ian Laker (),
Chun-Kai Huang () and
Allan Ernest Clark ()
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Ian Laker: University of Cape Town
Chun-Kai Huang: University of Cape Town
Allan Ernest Clark: University of Cape Town
Risk Management, 2017, vol. 19, issue 4, 301-322
Abstract Estimation in extreme financial risk is often faced with challenges such as the need for adequate distributional assumptions, considerations for data dependencies, and the lack of tail information. Bootstrapping provides an alternative that overcomes some of these challenges. It does not assume a distributional form and asymptotically replicates the empirical density for resampled data. Moreover, advanced bootstrapping can cater for dependencies and stationarity in the data. In this paper, we evaluate the use of dependent bootstrapping, both for the original financial time series and for its GARCH innovations (under the Gaussian and Student t noise assumptions), in forecasting value-at-risk and expected shortfall. We also assess the effect of using different window sizes for these procedures. The two datasets used are daily returns of the S&P500 from NYSE and the ALSI from JSE.
Keywords: Block bootstrap; Stationary bootstrap; Value-at-risk; Expected shortfall; GARCH (search for similar items in EconPapers)
JEL-codes: C13 C14 C32 C52 C53 G12 G17 (search for similar items in EconPapers)
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