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Bootstrapping Nonparametric Prediction Intervals for Conditional Value-at-Risk with Heteroscedasticity

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  • Emmanuel Torsen
  • Lema Logamou Seknewna

Abstract

Using bootstrap method, we have constructed nonparametric prediction intervals for Conditional Value-at-Risk for returns that admit a heteroscedastic location-scale model where the location and scale functions are smooth, and the function of the error term is unknown and is assumed to be uncorrelated to the independent variable. The prediction interval performs well for large sample sizes and is relatively small, which is consistent with what is obtainable in the literature.

Suggested Citation

  • Emmanuel Torsen & Lema Logamou Seknewna, 2019. "Bootstrapping Nonparametric Prediction Intervals for Conditional Value-at-Risk with Heteroscedasticity," Journal of Probability and Statistics, Hindawi, vol. 2019, pages 1-6, May.
    • Handle: RePEc:hin:jnljps:7691841
    DOI: 10.1155/2019/7691841
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    Cited by:

    1. Li, Jiang-Cheng & Tao, Chen & Li, Hai-Feng, 2022. "Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Emmanuel Torsen & Peter N. Mwita & Joseph K. Mung’atu, 2019. "A Three-Step Nonparametric Estimation of Conditional Value-At-Risk Admitting a Location-Scale Model," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-1.

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