Paper: arXiv 2407.15766

Authors: Shafique Ur Rehman, Touqeer Ahmad, Wu Dash Desheng, Amirhossein Karamoozian

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a solid quantitative framework combining eGARCH, Bulk-and-Tails Extreme Value Theory, and Copulas to estimate advanced risk measures (VaR, ES, RVaR) alongside Diebold-Yilmaz spillovers. It exhibits strong empirical rigor using crypto and traditional indices, though it relies primarily on standard, well-documented econometric pipelines.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze Risk Measures & Spillover between Cryptos & Financial Indices] --> B[Data Inputs: Cryptocurrencies & Financial Indices e.g., MSCI World, MSCI EM]
    B --> C[Marginal Modeling: Symmetric & Asymmetric GARCH e.g., eGARCH + Tail-Flexible EVT]
    C --> D[Joint Distribution: Copula Functions to capture bivariate dependencies]
    D --> E[Risk Estimation: Calculate VaR, ES, and RVaR for Portfolios]
    E --> F[Spillover Analysis: Diebold-Yilmaz Approach to quantify asset connectedness]
    F --> G[Key Outcomes: eGARCH-EVT-Copula best for VaR; High intra-crypto spillover but low impact on traditional indices]

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0 of 82 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.