Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches

Shafique Ur Rehman, Touqeer Ahmad, Wu Dash Desheng, Amirhossein Karamoozian
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Abstract

This study examines the interdependence between cryptocurrencies and international financial indices, such as MSCI World and MSCI Emerging Markets. We compute the value at risk, expected shortfall (ES), and range value at risk (RVaR) and investigate the dynamics of risk spillover. We employ a hybrid approach to derive these risk measures that integrate GARCH models, extreme value models, and copula functions. This framework uses a bivariate portfolio approach involving cryptocurrency data and traditional financial indices. To estimate the above risks of these portfolio structures, we employ symmetric and asymmetric GARCH and both tail flexible EVT models as marginal to model the marginal distribution of each return series and apply different copula functions to connect the pairs of marginal distributions into a multivariate distribution. The empirical findings indicate that the eGARCH EVT-based copula model adeptly captures intricate dependencies, surpassing conventional methodologies like Historical simulations and t-distributed parametric in VaR estimation. At the same time, the HS method proves superior for ES, and the t-distributed parametric method outperforms RVaR. Eventually, the Diebold-Yilmaz approach will be applied to compute risk spillovers between four sets of asset sequences. This phenomenon implies that cryptocurrencies reveal substantial spillover effects among themselves but minimal impact on other assets. From this, it can be concluded that cryptocurrencies propose diversification benefits and do not provide hedging advantages within an investor's portfolio. Our results underline RVaR superiority over ES regarding regulatory arbitrage and model misspecification. The conclusions of this study will benefit investors and financial market professionals who aspire to comprehend digital currencies as a novel asset class and attain perspicuity in regulatory arbitrage.
分析特定加密货币对全球金融指数的溢出效应:使用传统方法和 eGARCH-EVT-Copula 方法比较风险度量
本研究探讨了加密货币与 MSCI 世界指数(MSCI World)和 MSCI 新兴市场指数(MSCI Emerging Markets)等国际金融指数之间的相互依存关系。我们计算了风险价值、预期缺口(ES)和区间风险价值(RVaR),并研究了风险溢出的动态变化。我们采用一种混合方法来推导这些风险度量,该方法整合了 GARCH 模型、极值模型和 copula 函数。该框架采用了一种涉及加密货币数据和传统金融指数的双变量投资组合方法。为了估算这些投资组合结构的上述风险,我们采用对称和非对称 GARCH 模型以及尾部灵活的 EVT 模型作为边际来模拟每个收益序列的边际分布,并应用不同的 copula 函数将边际分布对连接成多变量分布。实证研究结果表明,基于 eGARCH EVT 的共函数模型能很好地捕捉错综复杂的依赖关系,在 VaRestimation 中超越了历史模拟和 t 分布参数等传统方法。与此同时,HS 方法被证明优于 ES 方法,t 分布参数方法优于 RVaR 方法。最终,Diebold-Yilmaz 方法将被应用于计算四组资产序列之间的风险溢出。这一现象意味着,加密货币之间会产生巨大的溢出效应,但对其他资产的影响却微乎其微。由此可以得出结论,在投资者的投资组合中,加密货币具有分散风险的优势,而不具有对冲优势。我们的研究结果凸显了 RVaR 在监管套利和模型失当方面优于 ES。这项研究的结论将使投资者和金融市场专业人士受益匪浅,他们都希望将数字货币理解为一种新型资产类别,并在监管套利方面获得洞察力。
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