The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility – A Two-Stage DCC-EGARCH Model Analysis

ArXiv ID: 2307.09137 “View on arXiv”

Authors: Unknown

Abstract

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish-Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

Keywords: EGARCH model, dynamic conditional correlation (DCC), value-at-risk (VaR), spillover effects, cryptocurrency volatility, Cryptocurrency and Stocks

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs a sophisticated two-stage multivariate EGARCH-DCC model with advanced volatility dynamics and Cornish-Fisher VaR, indicating high mathematical density, while its analysis of real financial data (2019-2020) with specific risk metrics like VaR and CFVaR demonstrates substantial empirical implementation.
  flowchart TD
    Start["Research Goal:<br>Impact of COVID-19 on Crypto/Stock<br>Volatility & Spillover Effects"] -->|Inputs| Data["Data Sources:<br>Jan 2019 - Dec 2020<br>Bitcoin, Ethereum, Dow, S&P 500"]
    
    Data --> Method["Methodology:<br>Two-Stage DCC-EGARCH Model<br>+ VaR & CFVaR Analysis"]
    
    Method --> Process["Computational Process:<br>1. Model Conditional Volatility<br>2. Measure Dynamic Correlations<br>3. Assess Portfolio Risk"]
    
    Process --> Findings["Key Findings:<br>• Significant Long/Short-term Spillovers<br>• Volatility surges after positive news<br>• DJI Highest Loss during COVID<br>• CFVaR: Negative Risk for most Crypto<br>• ETH showed unique resilience"]
    
    Findings --> End["Outcome:<br>Enhanced risk assessment for<br>crypto-stock portfolios during crises"]