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

Author

Listed:
  • Aubain Nzokem

    (Department of Mathematics & Statistics, York University, Toronto, ON M3J 1P3, Canada
    These authors contributed equally to this work.)

  • Daniel Maposa

    (Department of Statistics and Operations Research, University of Limpopo, Sovenga 0727, South Africa
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Aubain Nzokem & Daniel Maposa, 2024. "Fitting the Seven-Parameter Generalized Tempered Stable Distribution to Financial Data," JRFM, MDPI, vol. 17(12), pages 1-29, November.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:12:p:531-:d:1527176
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    References listed on IDEAS

    as
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    4. Marsaglia, George & Marsaglia, John, 2004. "Evaluating the Anderson-Darling Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i02).
    5. Michael Grabchak & Gennady Samorodnitsky, 2010. "Do financial returns have finite or infinite variance? A paradox and an explanation," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 883-893.
    6. Marsaglia, George & Tsang, Wai Wan & Wang, Jingbo, 2003. "Evaluating Kolmogorov's Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i18).
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