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Applied Financial Economics Volume 22, 2012 - Issue 16
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Original Articles

Approximation of skewed and leptokurtic return distributions

Matthias Scherer Department of Statistics, Econometrics and Mathematical Finance, School of Economics and Business Engineering, University of Karlsruhe and KIT, Karlsruhe, Germany
,
Svetlozar T. Rachev Department of Applied Mathematics and Statistics, Stony Brook University, USA; School of Economics and Business Engineering, Karlsruhe Institute of Technology, Germany; FinAnalytica
,
Young Shin Kim Department of Statistics, Econometrics and Mathematical Finance, School of Economics and Business Engineering, University of Karlsruhe and KIT, Karlsruhe, Germany
&
Frank J. Fabozzi EDHEC Business School, Nice, FranceCorrespondence[email protected]
Pages 1305-1316 | Published online: 05 Apr 2012

Citations (21)

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Read on this site (4)

Yunfei Xia & Michael Grabchak. (2022) Estimation and simulation for multivariate tempered stable distributions. Journal of Statistical Computation and Simulation 92:3, pages 451-475.
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Ahmad Arefi & Reza Pourtaheri. (2020) Multi-modal tempered stable distributions and prosses with applications to finance. Communications in Statistics - Theory and Methods 49:17, pages 4133-4149.
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J. Hambuckers & C. Heuchenne. (2017) A robust statistical approach to select adequate error distributions for financial returns. Journal of Applied Statistics 44:1, pages 137-161.
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Miguel A. Sordo, Marilia C. de Souza & Alfonso Suárez-Llorens. (2015) A new variability order based on tail-heaviness. Statistics 49:5, pages 1042-1061.
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Articles from other publishers (17)

Fumin Zhu, Michele Leonardo Bianchi, Young Shin Kim, Frank J. Fabozzi & Hengyu Wu. (2021) Learning for infinitely divisible GARCH models in option pricing. Studies in Nonlinear Dynamics & Econometrics 25:3, pages 35-62.
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Hasan Fallahgoul & Gregoire Loeper. (2019) Modelling tail risk with tempered stable distributions: an overview. Annals of Operations Research 299:1-2, pages 1253-1280.
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Peter Bossaerts, Shijie Huang & Nitin Yadav. (2020) Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk. Risks 8:4, pages 113.
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Nicolas Hardy, Nicolas S. Magner, Jaime Lavin, Rodrigo A. Cardenas & Mauricio Jara-Bertin. (2018) Small consequences of a major agreement: the MILA case. Academia Revista Latinoamericana de Administración 31:3, pages 486-518.
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Michele Leonardo Bianchi, Svetlozar T. Rachev & Frank J. Fabozzi. (2016) Calibrating the Italian Smile with Time-Varying Volatility and Heavy-Tailed Models. Computational Economics 51:3, pages 339-378.
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Julio Mulero, Miguel A. Sordo, Marilia C. de Souza & Alfonso Suárez-LLorens. (2017) Two stochastic dominance criteria based on tail comparisons. Applied Stochastic Models in Business and Industry 33:6, pages 575-589.
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Xiaoli Gong & Xintian Zhuang. (2017) Pricing foreign equity option under stochastic volatility tempered stable Lévy processes. Physica A: Statistical Mechanics and its Applications 483, pages 83-93.
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Xiaoli Gong & Xintian Zhuang. (2017) Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes. The North American Journal of Economics and Finance 40, pages 148-159.
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Skander Slim, Yosra Koubaa & Ahmed BenSaïda. (2017) Value-at-Risk under Lévy GARCH models: Evidence from global stock markets. Journal of International Financial Markets, Institutions and Money 46, pages 30-53.
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Miguel A. Sordo, Marilia C. de Souza & Alfonso Suárez-Llorens. (2016) Testing variability orderings by using Gini’s mean differences. Statistical Methodology 32, pages 63-76.
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Sergio Ortobelli, Tommaso Lando, Filomena Petronio & Tomas Tichý. (2016) Asymptotic stochastic dominance rules for sums of i.i.d. random variables. Journal of Computational and Applied Mathematics 300, pages 432-448.
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MICHELE LEONARDO BIANCHI, GIAN LUCA TASSINARI & FRANK J. FABOZZI. (2016) RIDING WITH THE FOUR HORSEMEN AND THE MULTIVARIATE NORMAL TEMPERED STABLE MODEL. International Journal of Theoretical and Applied Finance 19:04, pages 1650027.
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Michele Leonardo Bianchi. (2015) Are the log-returns of Italian open-end mutual funds normally distributed? A risk assessment perspective. Journal of Asset Management 16:7, pages 437-449.
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Michele Leonardo Bianchi & Frank J. Fabozzi. (2014) Discussion of ‘on simulation and properties of the stable law’ by Devroye and James. Statistical Methods & Applications 23:3, pages 353-357.
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Michele Leonardo Bianchi. (2014) Are the Log-Returns of Italian Open-End Mutual Funds Normally Distributed? A Risk Assessment Perspective. SSRN Electronic Journal.
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Michele Leonardo Bianchi, Frank J. Fabozzi & Svetlozar Rachev. (2014) Calibrating the Italian Smile with Time-Varying Volatility and Heavy-Tailed Models. SSRN Electronic Journal.
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Michele Leonardo Bianchi, Svetlozar Rachev & Frank J. Fabozzi. (2013) Tempered Stable Ornstein-Uhlenbeck Processes: A Practical View. SSRN Electronic Journal.
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