Markov Processes and the Distribution of Volatility: A Comparison Of Discrete and Continuous Specifications
Stephen J. Taylor
Lancaster University - Department of Accounting and Finance
Working Paper 99/001
Two mixtures of Normal distributions, created by persistent changes in volatility, are compared as models for asset returns. A Markov chain with two states for volatility is contrasted with an autoregressive Gaussian process for the logarithm of volatility. The conditional variances of asset returns are shown to have a bimodal distribution for the former process when volatility is persistent, that contrasts with a unimodal distribution for the latter process. A test procedure based upon this contrast shows that a lognormal distribution for Sterling/Dollar volatility is far more credible than only two volatility states.
JEL Classification: C22, C52, F31, G15
Date posted: February 26, 1999
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