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Volatility models for cryptocurrencies and applications in the options market

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  • Chi, Yeguang
  • Hao, Wenyan

Abstract

We investigate the effectiveness of various volatility models using the Bitcoin (BTC) and Ethereum (ETH) price series. Amongst the single-variate models, the GARCH model performs well both in sample and out of sample. Moreover, we do not observe any significant asymmetric volatility reponse to past returns in the GJR-GARCH model. Although the multi-variate VARMA-DCC-AGARCH model outperforms in sample, it performs worse out of sample than the single-variate GARCH model. Furthermore, the GARCH volatility forecast outperforms the option implied volatility in forecasting future realized volatility. We formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits for both BTC and ETH options. We interpret the strategy profitability as evidence for the pricing inefficiencies in the cryptocurrency options market during our sample period.

Suggested Citation

  • Chi, Yeguang & Hao, Wenyan, 2021. "Volatility models for cryptocurrencies and applications in the options market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:intfin:v:75:y:2021:i:c:s1042443121001359
    DOI: 10.1016/j.intfin.2021.101421
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    Cited by:

    1. S. Sapna & Biju R. Mohan, 2024. "Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 515-550, July.
    2. Nidhal Mgadmi & Azza Béjaoui & Wajdi Moussa & Tarek Sadraoui, 2022. "The Impact of the COVID-19 Pandemic on the Cryptocurrency Market," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 69(3), pages 343-359, September.
    3. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
    4. Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.

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