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Journal of Time Series Econometrics

Editor-in-Chief: Hidalgo, Javier

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CiteScore 2017: 0.25

SCImago Journal Rank (SJR) 2017: 0.236
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1941-1928
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Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation

David Ardia
  • Corresponding author
  • 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-2000 Neuchâtel, Switzerland
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  • De Gruyter OnlineGoogle Scholar
/ Keven Bluteau
  • Vrije Universiteit Brussel, Solvay Business School, Brussel, Belgium
  • Institute of Financial Analysis, University of Neuchâtel, Neuchâtel, Switzerland
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/ Lennart F. Hoogerheide
Published Online: 2018-07-21 | DOI: https://doi.org/10.1515/jtse-2017-0011

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

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About the article

Published Online: 2018-07-21


Citation Information: Journal of Time Series Econometrics, Volume 10, Issue 2, 20170011, ISSN (Online) 1941-1928, DOI: https://doi.org/10.1515/jtse-2017-0011.

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