Skip to main content
Log in

Computational Tools for the Analysis of Market Risk

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

The estimation and management of risk is an important and complex task faced by market regulators and financial institutions. Accurate and reliable quantitative measures of risk are needed to minimize undesirable effects on a given portfolio fromlarge fluctuations in market conditions. To accomplish this, a series of computational tools has beendesigned, implemented, and incorporated into MatRisk, an integratedenvironment for risk assessment developed in MATLAB. Besides standard measures, such as Value at Risk(VaR), the application includes other more sophisticated risk measures that address the inability of VaRproperly to characterize the structure of risk. Conditionalrisk measures can also be estimated for autoregressive models with heteroskedasticity, including some novel mixture models. These tools are illustrated with a comprehensive risk analysis of the Spanish IBEX35 stock index.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Artzner, P., Delbaen, F., Eber, J.-M., and Heath, D. (1997). Thinking coherently. Risk, 10(11), 68-71.

    Google Scholar 

  • Artzner, P., Delbaen, F., Eber, J.-M., and Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203-228.

    Google Scholar 

  • Bollerslev, T. (1986). Generalized autoregressive conditional volatility. Journal of Econometrics, 31, 307-327.

    Google Scholar 

  • Danielsson, J., Hartmann, P., and de Vries, C. (1998). The cost of conservatism. Risk, (January), 101-103.

  • Eberlein, E. and Keller, U. (1995). Hyperbolic distributions in finance. Bernoulli, 1, 281-299.

    Google Scholar 

  • Embrechts, P., Kluplelberg, C., and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer-Verlag, Berlin.

    Google Scholar 

  • Embrechts, P., Resnick, S., and Samorodnitsky, G. (1998). Living on the edge. Risk, (January), 96-100.

  • Engle, R.F. (1982). Autoregressive conditional heteroskedasticity wity estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.

    Google Scholar 

  • Hamilton, J.D. (1991). A quasi-Bayesian approach to estimating parameters for mixtures of normal distributions. Journal of Business and Economic Statistics, 9(1), 27-39.

    Google Scholar 

  • Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Hull, J. and White, A. (1997). Evaluating the impact of kurtosis and skewness on derivative prices. NetExposure, 3, 81-90.

    Google Scholar 

  • Hull, J. and White, A. (1998). Value at risk when daily changes in market variables are not normally distributed. Journal of Derivatives, 5(3), 9-19.

    Google Scholar 

  • Jarrow, R. (ed.) (1998). Volatility: New Estimation Techniques for Pricing Derivatives. Risk Books, London.

    Google Scholar 

  • Jorion, P. (1997). Value at Risk: The New Benchmark for Controlling Market Risk. McGraw-Hill, New York.

    Google Scholar 

  • Kuchler, U., Neumann, K., Soerensen, M., and Streller, A. (1994). Stock returns and hyperbolic distributions. Sonderforschungsbereich 373, Humboldt-Universtat zu Berlin, (23), 1-18.

  • McCulloh, J.H. (1996). Financial applications of stable distributions. In G.S. Maddala and C.R. Rao (eds.), Statistical Methods in Finance, Handbook of Statistics Vol. 14. North Holland.

  • Ormoneit, D. and Neuneier, R. (1999). Conditional value at risk. In Y.S. Abu-Mostafa, A.W. Lo, and A.S. Weigend (eds.), Proceedings of the Sixth International Conference in Computational Finance. MIT Press, Cambridge, U.S.A.

    Google Scholar 

  • Press, W., Teukolsky, W.T., Vetterling, S.A., and Flannery, B. (1993). Numerical Recipes in C. Cambridge University Press, Cambridge.

  • Weigend, A.S., Mangeas, M., and Srivastava, A.N. (1995). Nonlinear gated experts for time series: Discovering regimes and avoiding overfitting. International Journal of Neural Systems, 6, 373-399.

    Google Scholar 

  • Wong, C.S. and Li, W.K. (2000). On a mixture autoregressive model. Journal of the Royal Statistical Society B, 62, 95-115.

    Google Scholar 

  • Zeevi, A.J., Meir, R., and Adler, R.J. (1999). Non-linear models for time series using mixtures of autorregressive models. Preprint.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Suárez, A., Carrillo, S. Computational Tools for the Analysis of Market Risk. Computational Economics 21, 153–172 (2003). https://doi.org/10.1023/A:1022267720606

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1022267720606

Navigation