MODELING FINANCIAL SERIES DISTRIBUTIONS: A VERSATILE DATA FITTING APPROACH
| Autor: | Jen S. Shang, Pandu R. Tadikamalla |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Finance
business.industry Cumulative distribution function Statistical parameter Convolution of probability distributions Empirical distribution function Dirichlet distribution symbols.namesake Financial models with long-tailed distributions and volatility clustering symbols Econometrics Kurtosis Probability distribution business General Economics Econometrics and Finance Mathematics |
| Zdroj: | International Journal of Theoretical and Applied Finance. :231-251 |
| ISSN: | 1793-6322 0219-0249 |
| DOI: | 10.1142/s0219024904002475 |
| Popis: | The empirical distribution of common stock returns is a subject of interest to many researchers, as it often determines the validity of theoretical models proposed in the economics and finance studies. This paper brings to the attention the availability of two flexible systems of distributions for fitting data: the Johnson system of distributions and the Tadikamalla–Johnson system of distributions. We explore the feasibility of fitting the empirical distributions of several financial series to these two systems of distributions. Both systems of distributions are highly flexible and capable of accommodating all possible skewness and kurtosis values. The probability density function and the cumulative distribution function take on simple closed forms and appropriate transformations of the data lead to normal/logistic distributions. In addition, the parameter estimation procedures are easy to implement. When the results are compared with those of other data fitting models, in all cases tested, the proposed distributions provide a good fit to the empirical distribution of data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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