{"title":"Estimation of Models for Stock Returns","authors":"","doi":"10.1007/s10614-024-10580-x","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Composite distributions where volatility itself is assumed to be a random variable have been used to model stock returns. In this paper, we give details of estimation of these composite distributions when the volatility is assumed to follow an arbitrary distribution and the conditional distribution of stock returns given the volatility follows one of normal, Laplace, uniform, Student’s <em>t</em>, Cauchy, logistic of type I, logistic of type II, logistic of type III, logistic of type IV, generalized normal or skew normal distributions. The details given include estimating equations and observed information matrices. An application to Bitcoin exchange rate data is illustrated. Models taking volatility to follow gamma and Weibull distributions are shown to provide excellent fits.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":"4 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s10614-024-10580-x","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Composite distributions where volatility itself is assumed to be a random variable have been used to model stock returns. In this paper, we give details of estimation of these composite distributions when the volatility is assumed to follow an arbitrary distribution and the conditional distribution of stock returns given the volatility follows one of normal, Laplace, uniform, Student’s t, Cauchy, logistic of type I, logistic of type II, logistic of type III, logistic of type IV, generalized normal or skew normal distributions. The details given include estimating equations and observed information matrices. An application to Bitcoin exchange rate data is illustrated. Models taking volatility to follow gamma and Weibull distributions are shown to provide excellent fits.
期刊介绍：
Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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