Advertisement

Backtesting Derivative Portfolios with FHS

Chapter
  • 107 Downloads

Abstract

Filtered historical simulation provides the general framework to our backtests of portfolios of derivative securities held by a large sample of financial institutions. We allow for stochastic volatility and exchange rates. Correlations are maintained implicitly by our simulation procedure. Options are re-priced at each node. Overall results support the adequacy of our framework, but our VaR numbers are too high for swap portfolios at long horizons and too low for options and futures portfolios at short horizons.

Keywords

Stochastic Volatility Implied Volatility Historical Simulation Option Contract Short Horizon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barone Adesi G., F. Bourgoin and K. Giannopoulos (1998), Don’t Look Back, Risk, 11, August, pp. 100–104.Google Scholar
  2. Barone Adesi G. and K. Giannopoulos (1996), A Simplified Approach to the Conditional Estimation of Value at Risk, Futures and Options World, October, pp. 68–72.Google Scholar
  3. Barone Adesi G., K. Giannopoulos and L. Vosper (1999), VaR without Correlations for Non-linear Portfolios, Journal of Futures Markets, 19, August, pp. 583–602.CrossRefGoogle Scholar
  4. Basle Committee on Banking Supervision (1996), Supervisory Framework for the Use of Backtesting in Conjunction with the Internal Models Approach to Market Risk Capital Requirements, Basle.Google Scholar
  5. Bollerslev T. (1986), Generalised Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 31, pp. 307–327.CrossRefGoogle Scholar
  6. Butler J. S. and B. Schachter (1998), Estimating Value at Risk by Combining Kernel Estimation with Historical Simulation, Review of Derivatives Research, 1, pp. 371–390.Google Scholar
  7. Embrechts P., Kluppelberg C. and Mikosch T. (1997) Modelling Extreme Events for Insurance and Finance, Springer, Berlin.CrossRefGoogle Scholar
  8. Jamshidian F. and Y. Zhu (1997), Scenario Simulation Model: Theory and Methodology, Finance and Stochastics, 1, pp. 43–67.CrossRefGoogle Scholar
  9. Kendall M. (1953), The Analysis of Economic time-Series, Journal of the Royal Statistical Society, 96, pp. 11–25.CrossRefGoogle Scholar
  10. Ljung G. M. and G. E. P. Box (1978), On a Measure of Lack of Fit in Time Series Models, Biometrika, 67, pp. 297–303.CrossRefGoogle Scholar
  11. Longin F. (2000) From Value at Risk to Stress Testing: the Extreme Value Approach, Journal of Banking and Finance, 24, pp. 1097–1130.CrossRefGoogle Scholar
  12. Mandelbrot B. (1963), The Variation of Certain Speculative Prices, Journal of Business, 36, pp. 394–419.CrossRefGoogle Scholar
  13. Markowitz H. (1959), Portfolio Selection: Efficient Diversification of Investments, John Wiley, New York.Google Scholar
  14. RiskMetrics (1993), Technical Document. 1st edition, JP Morgan Publication (available on their website).Google Scholar
  15. Van den Goorbergh R. and P. Vlaar (1999) Value at Risk Analysis of Stock Returns: Historical Simulation, Variance Techniques or Tail Index Estimation?, Manuscript, Tilburg University.Google Scholar
  16. Vlaar P. (2000) Value at Risk Models for Dutch Bond Portfolios, Journal of Banking and Finance, 24, pp. 1131–1154.CrossRefGoogle Scholar

Copyright information

© Giovanni Barone Adesi 2014

Authors and Affiliations

There are no affiliations available

Personalised recommendations