Approximation of skewed and leptokurtic return distributions

Autor:	Young Shin Kim, Frank J. Fabozzi, Svetlozar T. Rachev, Matthias Scherer
Rok vydání:	2012
Předmět:	Economics and Econometrics Series (mathematics) Skewness Maximum likelihood Financial models with long-tailed distributions and volatility clustering Fast Fourier transform Econometrics Kurtosis Inference Finance Mathematics
Zdroj:	Applied Financial Economics. 22:1305-1316
ISSN:	1466-4305 0960-3107
Popis:	There is considerable empirical evidence that financial returns exhibit leptokurtosis and nonzero skewness. As a result, alternative distributions for modelling a time series of the financial returns have been proposed. A family of distributions that has shown considerable promise for modelling financial returns is the tempered stable and tempered infinitely divisible distributions. Two representative distributions are the classical tempered stable and the Rapidly Decreasing Tempered Stable (RDTS). In this article, we explain the practical implementation of these two distributions by (1) presenting how the density functions can be computed efficiently by applying the Fast Fourier Transform (FFT) and (2) how standardization helps to drive efficiency and effectiveness of maximum likelihood inference.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7324dbedcd06544edcb50430f1cd5bf6 https://doi.org/10.1080/09603107.2012.659342 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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