A robust VaR model under different time periods and weighting schemes

A robust VaR model under different time periods and weighting schemes This paper analyses several volatility models by examining their ability to forecast Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by using non parametric, semi parametric and parametric methods. In order to choose one model among the various forecasting methods, a two-stage backtesting procedure is implemented. In the first stage the unconditional coverage test is used to examine the statistical accuracy of the models. In the second stage a loss function is applied to investigate whether the differences between the models, that calculated accurately the VaR, are statistically significant. Under this framework, the combination of a parametric model with the historical simulation produced robust results across the sample periods, market capitalization schemes, trading positions and confidence levels and therefore there is a risk measure that is reliable. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

A robust VaR model under different time periods and weighting schemes

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2006 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-006-0010-y
Publisher site
See Article on Publisher Site

Abstract

This paper analyses several volatility models by examining their ability to forecast Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by using non parametric, semi parametric and parametric methods. In order to choose one model among the various forecasting methods, a two-stage backtesting procedure is implemented. In the first stage the unconditional coverage test is used to examine the statistical accuracy of the models. In the second stage a loss function is applied to investigate whether the differences between the models, that calculated accurately the VaR, are statistically significant. Under this framework, the combination of a parametric model with the historical simulation produced robust results across the sample periods, market capitalization schemes, trading positions and confidence levels and therefore there is a risk measure that is reliable.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Dec 29, 2006

References

  • Non-parametric VaR techniques. Myths and realities
    Barone-Adesi, G; Giannopoulos, K
  • A multi-country study of power ARCH models and national stock market returns
    Brooks, RD; Faff, RW; McKenzie, MD
  • Extreme value theory and Value-at-Risk: Relative performance in emerging markets
    Gençay, R; Selçuk, F
  • Value-at-Risk: Applying the extreme value approach to Asian markets in the recent financial turmoil
    Ho, L-C; Burridge, P; Cadle, J; Theobald, M
  • Testing for differences in the tails of stock-market returns
    Jondeau, E; Rockinger, M
  • Statistical inference using extreme order statistics
    Pickands, J
  • Forecasting volatility in financial markets: A review
    Poon, S-H; Granger, WJ

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