Skip to main content

Value at Risk and Backtesting

  • Chapter
  • First Online:
Statistics of Financial Markets

Part of the book series: Universitext ((UTX))

  • 4068 Accesses

Abstract

Value-at-Risk (VaR) is probably the most commonly known measure for quantifying and controlling the risk of a portfolio. Establishing VaR is of central importance to a credit institute. The description of risk is attained with the help of an “internal model”, whose job is to reflect the market risk of portfolios and similar uncertain investments over time. The objective parameter in the model is the probability forecast of portfolio changes over a given period.

Valor en riesgo y testeo retroactivo

El que busca la verdad corre el riesgo de encontrarla

Anyone who seeks the truth, risks to find it.

Manuel Vicent

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Breiman, L. (1973). Statistics: With a view towards application. Boston: Houghton Mifflin Company.

    Google Scholar 

  • Cizek, P., Härdle, W., & Weron, R. (2011). Statistical tools in finance and insurance (2nd ed.). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Feller, W. (1966). An introduction to probability theory and its application (Vol. 2). New York: Wiley.

    Google Scholar 

  • Franke, J., Härdle, W., & Hafner, C. (2011). Statistics of financial markets (3rd ed.). Berlin/ Heidelberg: Springer.

    Google Scholar 

  • Härdle, W., & Simar, L. (2012). Applied multivariate statistical analysis (3rd ed.). Berlin: Springer.

    Google Scholar 

  • Härdle, W., Müller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Berlin: Springer.

    Google Scholar 

  • Harville, D. A. (2001). Matrix algebra: Exercises and solutions. New York: Springer.

    Google Scholar 

  • Klein, L. R. (1974). A textbook of econometrics (2nd ed., 488 p.). Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • MacKinnon, J. G. (1991). Critical values for cointegration tests. In R. F. Engle & C. W. J. Granger (Eds.), Long-run economic relationships readings in cointegration (pp. 266–277). New York: Oxford University Press.

    Google Scholar 

  • Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. Duluth/London: Academic.

    Google Scholar 

  • RiskMetrics. (1996). J.P. Morgan/Reuters (4th ed.). RiskMetricsTM.

    Google Scholar 

  • Serfling, R. J. (2002). Approximation theorems of mathematical statistics. New York: Wiley.

    Google Scholar 

  • Tsay, R. S. (2002). Analysis of financial time series. New York: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Borak, S., Härdle, W.K., López-Cabrera, B. (2013). Value at Risk and Backtesting. In: Statistics of Financial Markets. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33929-5_14

Download citation

Publish with us

Policies and ethics