Volatility Persistence, Dynamic Co-Movements, and Portfolio Optimization in Digital Asset Markets: Disentangling Cross-Market Contagion from Internal Hedging Strategies
DOI:
https://doi.org/10.55578/jift.2606.009Keywords:
Cryptocurrency, Volatility Clustering, DCC-GARCH, Portfolio Optimization, Hedge Ratios, Bitcoin, Stellar, Internet Node Token, Cross-Market Contagion, Stablecoins, Tokenized Gold, Digital AssetsAbstract
This study investigates the time-varying volatility dynamics, persistence, and optimal portfolio allocation architecture within the digital asset ecosystem, explicitly separating external cross-market contagion from internal digital asset hedging strategies. Utilizing real-world daily historical return data from 2020 to 2025 for a diverse basket of major (Bitcoin, Ethereum, Litecoin) and niche (Stellar, Internet Node Token, Exclusive Coin) digital assets, we employ a multivariate DCC-GARCH framework evaluated under two distinct currency configurations (Euro and US Dollar numeraires) alongside native on-chain alternative hedges. The empirical findings reveal high volatility persistence and long-memory characteristics across all assets, with volatility half-lives extending up to 22.8 days for specialized tokens, confirming that shocks to the system decay slowly. The regime-dependent dynamic conditional correlations demonstrate that cross-asset integration is highly pro-cyclical, intensifying severely during systemic market drawdowns and rapidly eroding traditional diversification benefits among core protocols. To counteract this systematic risk, the study introduces native digital hedges, demonstrating that the stablecoin Tether (USDT) offers superior downside protection and variance reduction (up to 74.15%) over traditional fiat exchange rates, while tokenized gold (PAXG) provides a robust, uncorrelated safe-haven overlay against macro-structural shocks. Significantly, out-of-sample optimization metrics confirm that the dynamic Global Minimum Variance Portfolio (GMVP) heavily outperforms static benchmarks, achieving a 66.19% risk reduction by rebalancing asset exposures daily in response to shifting conditional variances. The study suggests that the Global Minimum Variance Portfolio is not composed solely of the largest caps (BTC/ETH). Instead, significant weight must be shifted toward assets with lower unconditional variance (like STR) or distinct covariance structures (like INT) to achieve the true efficient frontier. The optimal hedge ratios (OHR) and optimal portfolio weights (OPW) demonstrates that while the crypto market is highly integrated, strategic allocation to uncorrelated assets like INT and stable performers like STR can significantly improve risk-adjusted returns. The study highlights the role of cryptocurrencies as not isolated digital assets but as systemic components with contagion effects that are increasingly significant for regulators, monetary authorities, and investors. By analyzing these cross-market dynamics during a period marked by economic volatility, including the COVID-19 pandemic and global inflationary pressures, this research provides valuable insights into the evolving relationship between cryptocurrencies and traditional financial assets. These findings provide critical insights for investors seeking to minimize portfolio variance and hedge against downside risk in the volatile cryptocurrency market. Furthermore, the allocation mechanism exposes a fundamental theoretical boundary in asset insulation, proving that portfolio optimization models will aggressively restrict high-correlation diversifiers if the underlying instrument carries unacceptable standalone baseline noise. This research provides a self-contained, digital-first risk mitigation toolkit for international asset allocators while offering critical insights for macroprudential watchdogs tasked with stablecoin regulation and systemic risk oversight.
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The data underlying this research will be made available by the authors upon reasonable request to the corresponding author subject to a formal request outlining the intended use, which will be reviewed and approved by the research team.
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Copyright (c) 2026 Beauty Igbinovia, Shiloh Akpan, Imran Enike Abu, Emoabino Muhammed, David Umoru (Author)
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