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Modelling Stock Market Excess Returns by Markov Modulated Gaussian Noise

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Author Info
Jonathan Manton
Anton Muscatelli
Vikram Krishnamurthy
Stan Hurn

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Abstract

A basic analysis of stock market excess return data shows both linear and non-linear dependence present. Previous papers have used this to argue that it must therefore be possible to predict future values. However, this paper shows that the linear and non-linear dependence can be explained by simply allowing the mean and variance of Gaussian noise to be modulated by a (typically 3 state) hidden Markov model. Attempting to fit a Markov modulated AR process proved fruitless; the conclusion is that there is no AR-predictability present in excess return data.

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Paper provided by Department of Economics, University of Glasgow in its series Working Papers with number 9806.

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Handle: RePEc:gla:glaewp:9806

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  3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
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  8. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 5(2), pages 199-242. [Downloadable!] (restricted)
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  12. David M. Cutler & James M. Poterba & Lawrence H. Summers, 1990. "Speculative Dynamics," NBER Working Papers 3242, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  13. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-313, September. [Downloadable!] (restricted)
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  14. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Blackwell Publishing, vol. 61(2), pages 247-64, April. [Downloadable!] (restricted)
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