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Models for the Interest Rate and Interest Rate Derivatives

  • Szymon Borak
  • Wolfgang Karl Härdle
  • Brenda López-Cabrera
Chapter
Part of the Universitext book series (UTX)

Abstract

Pricing interest rate derivatives fundamentally depends on the underlying term structure. The often made assumptions of constant risk free interest rate and its independence of equity prices will not be reasonable when considering interest rate derivatives. Just as the dynamics of a stock price are modeled via a stochastic process, the term structure of interest rates is modeled stochastically. As interest rate derivatives have become increasingly popular, especially among institutional investors, the standard models for the term structure have become a core part of financial engineering. It is therefore important to practice the basic tools of pricing interest rate derivatives. For interest rate dynamics, there are one-factor and two-factor short rate models, the Heath Jarrow Morton framework and the LIBOR Market Model.

References

  1. Breiman, L. (1973). Statistics: With a view towards application. Boston: Houghton Mifflin Company.Google Scholar
  2. Cizek, P., Härdle, W., & Weron, R. (2011). Statistical tools in finance and insurance (2nd ed.). Berlin/Heidelberg: Springer.Google Scholar
  3. Feller, W. (1966). An introduction to probability theory and its application (Vol. 2). New York: Wiley.Google Scholar
  4. Franke, J., Härdle, W., & Hafner, C. (2011). Statistics of financial markets (3rd ed.). Berlin/ Heidelberg: Springer.Google Scholar
  5. Härdle, W., & Simar, L. (2012). Applied multivariate statistical analysis (3rd ed.). Berlin: Springer.Google Scholar
  6. Härdle, W., Müller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Berlin: Springer.Google Scholar
  7. Harville, D. A. (2001). Matrix algebra: Exercises and solutions. New York: Springer.Google Scholar
  8. Klein, L. R. (1974). A textbook of econometrics (2nd ed., 488 p.). Englewood Cliffs: Prentice Hall.Google Scholar
  9. 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
  10. Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. Duluth/London: Academic.Google Scholar
  11. RiskMetrics. (1996). J.P. Morgan/Reuters (4th ed.). RiskMetricsTM.Google Scholar
  12. Serfling, R. J. (2002). Approximation theorems of mathematical statistics. New York: Wiley.Google Scholar
  13. Tsay, R. S. (2002). Analysis of financial time series. New York: Wiley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Szymon Borak
    • 1
  • Wolfgang Karl Härdle
    • 1
  • Brenda López-Cabrera
    • 1
  1. 1.Humboldt-Universität zu Berlin Ladislaus von Bortkiewicz Chair of StatisticsC.A.S.E. Centre for Applied Statistics and Economics School of Business and EconomicsBerlinGermany

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