Cryptocurrency Portfolio Optimization Under Volatility and Tail Risk

Cryptocurrency markets combine persistent volatility, heavy tails, and recurrent stress episodes. This study asks whether portfolio optimization is sufficient to manage these risks for Bitcoin, Ethereum, Binance Coin, Solana, and XRP, using 1454 daily observations from January 2022 to January 2026. Student-t GARCH and DCC-GARCH forecasts feed static and rolling allocation rules, evaluated out of sample and net of transaction costs against equal-weight, minimum-variance, risk-parity, and minimum-CVaR benchmarks; downside risk is backtested with Kupiec and Christoffersen tests, and three anomaly detectors are validated against four crisis windows, including expanding-window refits. Volatility persistence ranges from 0.912 to 1.000, and average conditional correlations are higher inside four crisis windows than outside (0.76 vs. 0.66), a contrast that exceeds placebo windows but is small relative to neighbouring days. In sample, maximum-Sharpe weights raise the Sharpe ratio (0.88 vs. 0.76) without statistical significance; out of sample, their point estimate is lower than that of equal weighting (0.92 vs. 1.15), a difference that is statistically inconclusive. Risk-based rules cut volatility by about 19% and maximum drawdown by 10 points, whereas GARCH–DCC allocation adds turnover without further gains, and minimum-CVaR cannot be distinguished from minimum-variance allocation in this sample, the tests having limited power. Isolation Forest flags are about 8 times more concentrated in crisis windows than chance, and about 10 times when refitted on an expanding window. Optimization lowers risk, whereas evidence that it improves risk-adjusted performance is inconclusive.