A Test of Financial Time-Series Data to Discriminate Among Lognormal, Gaussian and Square-Root Random Walks

Authors: Yuri Heymann

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.

Comments: 11 Pages.

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Submission history

[v1] 2014-03-11 11:30:25
[v2] 2015-01-19 13:59:50 (removed)
[v3] 2015-02-19 12:33:17 (removed)
[v4] 2015-07-08 07:59:15

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