Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation

David Ardia, Keven Bluteau and Hoogerheide Lennart F. ()
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Hoogerheide Lennart F.: Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Journal of Time Series Econometrics, 2018, vol. 10, issue 2, 9

Abstract: Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE and perform a Monte Carlo experiments to compare their performance in the case of high/extreme autocorrelation. In particular, we propose an application to risk management where we assess the precision of the value-at-risk measure when the underlying risk model is estimated by simulation-based methods. Overall, heteroscedasticity and autocorrelation estimators with prewhitening perform best in the presence of large/extreme autocorrelation.

Keywords: bootstrap; GARCH; HAC kernel; numerical standard error (NSE); Monte Carlo; Markov chain Monte Carlo (MCMC); spectral density; value-at-risk; Welch (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/jtse-2017-0011

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