Voigt Distribution and Its Heavy-tail Modeling Ability for Cryptocurrencies

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Voigt Distribution and Its Heavy-tail Modeling Ability for Cryptocurrencies

Popis výsledku v původním jazyce

Voigt distribution is a convolution of a Cauchy distribution and a Gaussian distribution. This distribution has been so far widely used in technical disciplines. The inclusion of the Cauchy distribution into the mixture makes it a possible alternative for modeling heavy tail properties commonly present in financial data. In our contribution, the close form of distributional characteristics of the distribution like characteristic function (CF), probability density function (PDF) and cumulative distribution function (CDF) is derived. Due to their numerical complexity, these functions do not provide any practical usage for the maximum likelihood estimation (MLE) of its parameters. Therefore, we propose the use of Fast Fourier Transform (FFT) to make the parameter estimation via MLE technique more numerically feasible. This approach is then verified on the returns of cryptocurrencies which tend to have more heavy tails than those of ordinary heavy tail distributions. The results we obtained with this method are quite interesting.

Název v anglickém jazyce

Voigt Distribution and Its Heavy-tail Modeling Ability for Cryptocurrencies

Popis výsledku anglicky

Voigt distribution is a convolution of a Cauchy distribution and a Gaussian distribution. This distribution has been so far widely used in technical disciplines. The inclusion of the Cauchy distribution into the mixture makes it a possible alternative for modeling heavy tail properties commonly present in financial data. In our contribution, the close form of distributional characteristics of the distribution like characteristic function (CF), probability density function (PDF) and cumulative distribution function (CDF) is derived. Due to their numerical complexity, these functions do not provide any practical usage for the maximum likelihood estimation (MLE) of its parameters. Therefore, we propose the use of Fast Fourier Transform (FFT) to make the parameter estimation via MLE technique more numerically feasible. This approach is then verified on the returns of cryptocurrencies which tend to have more heavy tails than those of ordinary heavy tail distributions. The results we obtained with this method are quite interesting.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 37th International Conference on Mathematical Methods in Economics 2019,

ISBN

978-80-7394-760-6

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

139-144

Název nakladatele

Jihočeská universita v Českých Budějovicích

Místo vydání

České Budějovice

Místo konání akce

Ceske Budejovice

Datum konání akce

11. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000507570400022