Sains Malaysiana 49(3)(2020): 703-712

http://dx.doi.org/10.17576/jsm-2020-4903-25

Modeling the Volatility of Cryptocurrencies: An Empirical Application of Stochastic Volatility Models

(Pemodelan Kemeruapan Mata Wang Kripto: Pengaplikasian Empirik Model Kemeruapan Stokastik)

MAMOONA ZAHID & FARHAT IQBAL*

Department of Statistics, University of Balochistan, Quetta, Pakistan

Received: 8 April 2019/Accepted: 10 November 2019

ABSTRACT

This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies.

Keywords: Bayesian model comparison; cryptocurrency; jumps; leverage; stochastic volatility

ABSTRAK

Kertas ini membandingkan beberapa model kemeruapan stokastik (SV) untuk pemodelan danpenganggaran kemeruapan empat modal mata wang kripto (Bitcoin, Ethereum, Ripple dan Litecoin). Model standard SV, model dengan hujung berat dan inovasi purata pergerakan, model dengan lompatan, kesan pengaruh dan kemeruapan dalam min diambil kira. Faktor Bayes untuk model penyuaian selalunya menyebelahi model SV hujung berat. Prestasi peramalan model ini didapati superior daripada model lain yang dibandingkan. Secara keseluruhan, keputusan kajian ini mencadangkan penggunaan model kemeruapan stokastik hujung berat untuk pemodelan penganggaran kemeruapan mata wang kripto.

Kata kunci: Kemeruapan stokastik; lompatan; mata wang kripto; perbandingan model Bayesi; pengaruh

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*Corresponding author; email: farhatiqb@gmail.com