Dynamic Hedging Strategies in Clean and Dirty Cryptocurrency Markets: Analyzing Volatility and Portfolio Optimization with TVP-VAR

Abstract

This study investigates connectivity, defined as the degree of interdependence and volatility spillovers among cryptocurrencies, in order to better understand their behavior during periods of financial stress. The methodological framework is based on the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, which enables a flexible analysis of the evolving dynamic relationships among financial assets over time.
The main objective is to assess the role of three categories of cryptocurrencies so-called "dirty" cryptocurrencies (Bitcoin and Ethereum), "clean" cryptocurrencies (Algorand and Cardano), and stablecoins (USDT and USDC) in risk management and their effectiveness as hedging instruments in diversified portfolios, particularly during periods of heightened uncertainty such as the COVID-19 pandemic and the Russia–Ukraine conflict.
Using daily data from July 2019 to July 2024, the analysis reveals strong interconnectedness among volatile cryptocurrencies, especially between Bitcoin and Ethereum. In contrast, stablecoins exhibit greater stability and resilience to shocks. These findings highlight the relevance of accounting for dynamic interlinkages between assets when designing portfolios that are resilient to financial crises.