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

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  • Ardia David

    (Institute of Financial Analysis, University of Neuchâtel, Neuchâtel, Switzerland; Department of Finance, Insurance and Real Estate, Laval University, Québec City, Canada; University of Neuchâtel, Rue A.-L. Breguet 2, CH-2000Neuchâtel, Switzerland)

  • Bluteau Keven

    (Vrije Universiteit Brussel, Solvay Business School, Brussel, Belgium)

  • Hoogerheide Lennart F.

    (Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands)

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.

Suggested Citation

  • Ardia David & Bluteau Keven & Hoogerheide Lennart F., 2018. "Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation," Journal of Time Series Econometrics, De Gruyter, vol. 10(2), pages 1-9, July.
    • Handle: RePEc:bpj:jtsmet:v:10:y:2018:i:2:p:9:n:1
    DOI: 10.1515/jtse-2017-0011
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    Cited by:

    1. Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org.

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