Fitting the Seven-Parameter Generalized Tempered Stable Distribution to Financial Data

Aubain Nzokem () and Daniel Maposa
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Aubain Nzokem: Department of Mathematics & Statistics, York University, Toronto, ON M3J 1P3, Canada
Daniel Maposa: Department of Statistics and Operations Research, University of Limpopo, Sovenga 0727, South Africa

JRFM, 2024, vol. 17, issue 12, 1-29

Abstract: This paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes maximum-likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavy-tailed data (Bitcoin and Ethereum returns) and two peaked data (S&P 500 and SPY ETF returns). For each historical data, the estimation results show that six-parameter estimations are statistically significant except for the local parameter, μ . The goodness of fit was assessed through Kolmogorov–Smirnov, Anderson–Darling, and Pearson’s chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p -value and outperforms the Kobol, Carr–Geman–Madan–Yor, and bilateral Gamma distributions.

Keywords: Generalized Tempered Stable (GTS); fractional Fourier transform (FRFT); function characteristic; Kolmogorov–Smirnov (K-S); maximum-likelihood estimation (MLE) (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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