A Note on Estimating the Parameters of the Diffusion-Jump Model of Stock Returns
讨论股票收益分布的非正态性，指出传统对数正态假设与实证中厚尾和零附近集中现象不符，并回顾了几种替代分布模型，但均未获普遍接受。
The search for a distribution which accurately describes the behavior of stock price returns has generated a considerable amount of controversy. While it is well known that the traditionally used assumption of lognormality deviates in systematic ways from the empirically observed—the latter has fatter tails and a larger concentration of mass near zero—none of the alternatives that have been proposed over the years (Stable Paretian—Mandelbrot [5], Poisson mixture of; lognormal distributions—Press [10], scaled T distribution—Praetz [9], lognormal with nonstationary variance—Rosenberg [11], subordinate stochastic process—Clark [2]) has gained general acceptance.