Skip to main content

Abstract

A rolling analysis of a time series model is often used to assess the model’s stability over time. When analyzing financial time series data using a statistical model, a key assumption is that the parameters of the model are constant over time. However, the economic environment often changes considerably, and it may not be reasonable to assume that a model’s parameters are constant. A common technique to assess the constancy of a model’s parameters is to compute parameter estimates over a rolling window of a fixed size through the sample. If the parameters are truly constant over the entire sample, then the estimates over the rolling windows should not be too different. If the parameters change at some point during the sample, then the rolling estimates should capture this instability.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

9.6 References

  • Alexander, C. (2001). Market Models: A Guide to Financial Data Analysis. John Wiley & Sons, Chichester, UK.

    Google Scholar 

  • Bauer, R.J. and J.R. Dahlquist (1999). Techincal Market Indicators: Analysis & Performance. John Wiley & Sons, New York.

    Google Scholar 

  • Banerjee, A. R. Lumsdaine and J.H. Stock (1992). “Recursive and Sequential Tests of the Unit Root and Trend Break Hypothesis: Theory and International Evidence,” Journal of Business and Economic Statistics, 10(3), 271–288.

    Article  Google Scholar 

  • Colby, R.W. and T.A Meyers (1988). The Encyclopedia of Technical Market Indicators. McGraw-Hill, New York.

    Google Scholar 

  • Dacorogna, M.M., R. Gençay, U.A. Müller, R.B. Olsen, and O.V. Pictet (2001). An Introduction to High-Frequency Finance. Academic Press, San Diego.

    Google Scholar 

  • Diebold, F.X. and R.S. Mariano (1995). “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253–263.

    Article  Google Scholar 

  • Shiller, R. (1998). Irrational Exuberance. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Zumbach, G.O., and U.A. Müller (2001). “Operators on Inhomogeneous Time Series,” International Journal of Theoretical and Applied Finance, 4, 147–178.

    MathSciNet  Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

(2006). Rolling Analysis of Time Series. In: Modeling Financial Time Series with S-PLUS®. Springer, New York, NY. https://doi.org/10.1007/978-0-387-32348-0_9

Download citation

Publish with us

Policies and ethics