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Cointegration

  • David Ruppert
  • David S. Matteson
Part of the Springer Texts in Statistics book series (STS)

Abstract

Suppose one could find a stock whose price (or log-price) series was stationary and therefore mean-reverting. This would be a wonderful investment opportunity. Whenever the price was below the mean, one could buy the stock and realize a profit when the price returned to the mean. Similarly, one could realize profits by selling short whenever the price was above the mean. Alas, returns are stationary but not prices. We have seen that log-prices are integrated. However, not all is lost. Sometimes one can find two or more assets with prices so closely connected that a linear combination of their prices is stationary. Then, a portfolio with weights assigned by the cointegrating vector, which is the vector of coefficients of this linear combination, will have a stationary price. Cointegration analysis is a means for finding cointegration vectors.

Keywords

Trading Strategy Hedge Fund Statistical Arbitrage Vector Error Correction Model Closing Price 
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. Alexander, C. (2001) Market Models: A Guide to Financial Data Analysis, Wiley, Chichester.Google Scholar
  2. Alexander, C., Giblin, I., and Weddington, W. III (2001) Cointegration and Asset Allocation: A New Hedge Fund, ISMA Discussion Centre Discussion Papers in Finance 2001–2003.Google Scholar
  3. Enders, W. (2004) Applied Econometric Time Series, 2nd ed., Wiley, New York.Google Scholar
  4. Hamilton, J. D. (1994) Time Series Analysis, Princeton University Press, Princeton, NJ.zbMATHGoogle Scholar
  5. Johansen, S. (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59, 1551–1580.CrossRefzbMATHMathSciNetGoogle Scholar
  6. Johansen, S. (1995) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press, New York.CrossRefzbMATHGoogle Scholar
  7. Johansen, S., and Juselius, K. (1990) Maximum likelihood estimation and inference on cointegration — With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 2, 169–210.CrossRefGoogle Scholar
  8. Pfaff, B. (2006) Analysis of Integrated and Cointegrated Time Series with R, Springer, New York.zbMATHGoogle Scholar
  9. Phillips, P. C. B., and Ouliaris, S. (1990) Asymptotic properties of residual based tests for cointegration. Econometrica, 58, 165–193.CrossRefzbMATHMathSciNetGoogle Scholar
  10. Pole, A. (2007) Statistical Arbitrage, Wiley, Hoboken, NJ.Google Scholar
  11. Vidyamurthy, G. (2004) Pairs Trading, Wiley, Hoboken, NJ.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David Ruppert
    • 1
  • David S. Matteson
    • 2
  1. 1.Department of Statistical Science and School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

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