Advertisement

Financial Time Series Models

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

Abstract

This chapter deals with financial time series analysis. The statistical properties of asset and return time series are influenced by the media (daily news on the radio, television and newspapers) that inform us about the latest changes in stock prices, interest rates and exchange rates. This information is also available to traders who deal with immanent risk in security prices. It is therefore interesting to understand the behaviour of asset prices. Economic models on the pricing of securities are mostly based on theoretical concepts which involve the formation of expectations, utility functions and risk preferences. Here we concentrate on the empirical facts. Firstly, given a data set we aim to specify an appropriate model reflecting the main characteristics of the empirically observable stock price process and we wish to know whether the assumptions underlying the model are fulfilled in reality or whether the model has to be modified. A new model on the stock price process could possibly effect the function of the markets. To this end we apply statistical tools to empirical data and start with considering the concepts of univariate analysis before moving on to multivariate time series.

Keywords

Asset Price Stock Prex Risk Preference Multivariate Time Series Daily News 
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.

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

Personalised recommendations