Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models

Enrique Jiménez-Rodríguez,Ángeles Cebrián-Hernández

doi:10.3390/math9030267

Abstract

Since the launch of Bitcoin, there has been a lot of controversy surrounding what asset class it is. Several authors recognize the potential of cryptocurrencies but also certain deviations with respect to the functions of a conventional currency. Instead, Bitcoin’s diversifying factor and its high return potential have generated the attention of portfolio managers. In this context, understanding how its volatility is explained is a critical element of investor decision-making. By modeling the volatility of classic assets, nonlinear models such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) offer suitable results. Therefore, taking GARCH(1,1) as a reference point, the main aim of this study is to model and assess the relationship between the Bitcoin volatility and key financial environment variables through a Conditional Correlation (CC) Multivariate GARCH (MGARCH) approach. For this, several commodities, exchange rates, stock market indices, and company stocks linked to cryptocurrencies have been tested. The results obtained show certain heterogeneity in the fit of the different variables, highlighting the uncorrelation with respect to traditional safe haven assets such as gold and oil. Focusing on the CC-MGARCH model, a better behavior of the dynamic conditional correlation is found compared to the constant.

Highlights

If the correlation coefficients between Bitcoin and the different assets are observed, it is noted that technology companies such as Riot Blockchain Inc (RIOT) (0.689 *), KBR (0.783 **), or NVDA (0.977 **) and electronic payment methods like VISA (0.831 **) are those that seem to be more related to cryptocurrency
For the development of the models, the study has focused on the Conditional Correlation Multivariate GARCH (MGARCH) approach
The work coincides with previous studies by defining the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) standard model as a suitable alternative for Bitcoin modeling, it does not reach the efficiency presented by the constant conditional correlation (CCC), Dynamic Conditional Correlation (DCC), and varying conditional correlation (VCC) MGARCH models

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the midst of the global financial crisis of 2008, Satoshi Nakamoto published the Bitcoin project. The white paper by Nakamoto [1] described a digital currency based on a sophisticated peer-to-peer (p2p) protocol that allowed online payments to be sent directly to a recipient without going through a financial institution. A potential nonsovereign asset fully decentralized and isolated from the uncertainties of a specific country or market was presented as a great value proposition [2], which took even more value, if possible, during the European sovereign debt crisis of 2010–2013 [3]. The market capitalization closed the year exceeding USD 10 billion and the price of Bitcoin reached over a thousand dollars [4,5]

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