Journal of Financial and Quantitative Analysis · 1983 · 285 citations · 10 references
EngineeringFinancial MathematicsAsset PricingPoisson MixtureMarket TrendFinancial Time Series AnalysisStochastic ProcessesStatistical DistributionStochastic SystemsStatisticsFinancial EconometricsEconomicsQuantitative FinanceStochastic VolatilityFinanceFinancial EconomicsSimplified Jump ProcessBusinessMarginal ChangeInterest Rate Modeling
Accurately modeling stock return distributions remains a key issue; recent focus has been on Poisson mixture models that capture leptokurtic behavior by decomposing price changes into normal and abnormal components. The model represents normal price changes with a lognormal diffusion and abnormal jumps with a Poisson process. See references [12], [3], [8], [10], [5], and [1].
The specification of a statistical distribution which accurately models the behavior of stock returns continues to be a salient issue in financial economics. With the introduction of arithmetic and geometric Brownian motion models, much attention has recently focused on a Poisson mixture of distributions as an appropriate specification of stock returns. For example, see [12], [3], [8], [10], [5], and [1]. Consistent with empirical evidence, these models yield leptokurtic security return distributions and, furthermore, the specification has much economic intuition. In particular, one may always decompose the total change in stock price into “normal” and “abnormal” components. The “normal” change may be due to variation in capitalization rates, a temporary imbalance between supply and demand, or the receipt of any other information which causes marginal price changes. This component is modelled as a lognormal diffusion process. The “abnormal” change is due to the receipt of any information which causes a more than marginal change in the price of the stock and is usually modeled as a Poisson process.
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