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      Tail probabilities for short-term returns on stocks

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          Abstract

          We consider the tail probabilities of stock returns for a general class of stochastic volatility models. In these models, the stochastic differential equation for volatility is autonomous, time-homogeneous and dependent on only a finite number of dimensional parameters. Three bounds on the high-volatility limits of the drift and diffusion coefficients of volatility ensure that volatility is mean-reverting, has long memory and is as volatile as the stock price. Dimensional analysis then provides leading-order approximations to the drift and diffusion coefficients of volatility for the high-volatility limit. Thereby, using the Kolmogorov forward equation for the transition probability of volatility, we find that the tail probability for short-term returns falls off like an inverse cubic. Our analysis then provides a possible explanation for the inverse cubic fall off that Gopikrishnan et al. (1998) report for returns over 5-120 minutes intervals. We find, moreover, that the tail probability scales like the length of the interval, over which the return is measured, to the power 3/2. There do not seem to be any empirical results in the literature with which to compare this last prediction.

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          The Pricing of Options and Corporate Liabilities

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            The Variation of Certain Speculative Prices

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              The Pricing of Options on Assets with Stochastic Volatilities

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                Author and article information

                Journal
                22 September 2018
                Article
                10.13140/RG.2.2.18816.28165
                1809.08416
                57b4f3d3-19b3-45c6-b109-7d3207e29e7a

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                This is an old preprint, this version dating from 2 March 2003
                q-fin.ST

                Statistical finance
                Statistical finance

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