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A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices
Robert C. Blattberg,Nicholas J. Gonedes +1 more
- pp 25-61
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In this article, the student and stable models were used to compare the rates of return of different stock price models, including the Student Distribution and the Stable Distribution, in terms of the Likelihood Ratio and the Fama-Roll.Abstract:
The following sections are included:INTRODUCTIONPROPERTIES OF THE STUDENT AND SYMMETRIC-STABLE DISTRIBUTIONSDefinitions and Properties of the Student and Stable ModelsSome Implications of the Student and Stable Models for Empirical and Theoretical WorkAdditional RemarksMODELS FOR RATES OF RETURNDerivation of the Student and Stable Models: SummaryOther Stock Price ModelsMETHODS FOR MODEL COMPARISONThe Likelihood RatioStabilityESTIMATION OF THE MODEL'S PARAMETERSM.L.E. for the Student DistributionFama-Roll Estimates for the Stable DistributionESTIMATION RESULTSThe Actual DataThe Design of the Monte Carlo StudyDiscussion of the Simulation ResultsResults for Rates of ReturnSUMMARYAPPENDIX A DERIVATIONS OF THE STUDENT AND STABLE MODELSAPPENDIX B PROPERTIES OF THE UNIFORM RANDOM NUMBERS USED IN THE SIMULATIONSread more
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Enhancement of the applicability of markowitz's portfolio optimization by utilizing random matrix theory
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Real estate is not normal: A fresh look at real estate return distributions
TL;DR: In this paper, investment risk models with infinite variance provide a better description of distributions of individual property returns in the Russell-NCREIF data base from 1980 to 1992 than normally distributed risk models.
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Tests of Mean-Variance Spanning
Raymond Kan,Guofu Zhou +1 more
TL;DR: In this article, the authors provide geometric interpretations of three asymptotic tests (likelihood ratio, Wald, and Lagrange multiplier) of mean-variance spanning under the normality assumption.
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A General Distribution for Describing Security Price Returns
TL;DR: The GB2 distribution is an extremely flexible distribution, containing a large number of well-known distributions, such as the lognormal, log-t, and log-Cauchy distribu tions, as special or limiting cases and allowing large, even infinite, higher moments.
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Measuring Tail Thickness to Estimate the Stable Index α: A Critique
TL;DR: In this article, it was shown that a generalized Pareto or simple PAREto tail-index estimate above 2 is not a strong argument against infinite-variance stable distributions.
References
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Journal ArticleDOI
The Distribution of Share Price Changes
TL;DR: In this paper, the authors presented both theoretical and empirical evidence about a probability distribution which describes the behavior of share price changes, which is the only known simple distribution to fit changes in share prices, and provided a far better fit to the data than the stable Paretian, compound process, and normal distributions.
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Parameter Estimates for Symmetric Stable Distributions
Eugene F. Fama,Richard Roll +1 more
TL;DR: In this paper, estimators for the scale parameter and characteristic exponent of symmetric stable distributions are proposed and Monte Carlo studies of these estimators are reported. And the powers of various goodness-of-fit tests of a Gaussian null hypothesis against non-Gaussian stable alternatives are also investigated.