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

Yuri Heymann ()
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Yuri Heymann: Georgia Institute of Technology

Computational Statistics, 2016, vol. 31, issue 4, No 7, 1373-1383

Abstract: 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.

Keywords: Hypothesis testing; Lognormality; Random walk (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00180-015-0630-6

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