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A test of financial time-series data to discriminate among lognormal, Gaussian and square-root random walks

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  • Yuri Heymann

    (Georgia Institute of Technology)

Abstract

This paper aims to offer a testing framework for the structural properties of the Brownian motion of the underlying stochastic process of a time series. In particular, the test can be applied to financial time-series data and discriminate among the lognormal random walk used in the Black-Scholes-Merton model, the Gaussian random walk used in the Ornstein-Uhlenbeck stochastic process, and the square-root random walk used in the Cox, Ingersoll and Ross process. Alpha-level hypothesis testing is provided. This testing framework is helpful for selecting the best stochastic processes for pricing contingent claims and risk management.

Suggested Citation

  • Yuri Heymann, 2016. "A test of financial time-series data to discriminate among lognormal, Gaussian and square-root random walks," Computational Statistics, Springer, vol. 31(4), pages 1373-1383, December.
    • Handle: RePEc:spr:compst:v:31:y:2016:i:4:d:10.1007_s00180-015-0630-6
    DOI: 10.1007/s00180-015-0630-6
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    References listed on IDEAS

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    1. Svetlozar T. Rachev & Chufang Wu & Frank J. Fabozzi, 2007. "Empirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 21-31, May.
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