Authors - Ekleen Kaur Abstract - Traditional risk frameworks, including the Geometric Brownian Motion (GBM) and stationary GARCH models, fail to account for the "volatility bursts" and "flash crashes" endemic to the altcoin market. This study the third in a series on cryptoeconomic risk introduces a multi-asset Merton Jump-Diffusion (MJD) model integrated with an Exponentially Weighted Moving Average (EWMA) covariance matrix to model portfolio risk in altcoin-only environments. By focusing exclusively on high-beta altcoins (XRP, SOL, ADA) and we address a critical gap by excluding market-anchor assets to isolate long-tail volatility dynamics neglected in existing literature. We implement a dual-model approach: a baseline MJD simulation and a "Capped Return" MJD model designed to mitigate unrealistic exponential price paths in long-horizon forecasts. Our results using Monte Carlo Value-at-Risk simulations demonstrate that incorporating a Poisson-driven jump component (j = 2.0) significantly improves λthe capture of tail risk compared to continuous models indicating pathological exponential growth without suppressing crash dynamics. Our work provides a technically rigorous framework for managing portfolios in decentralized, high-liquidity-shock environments. Backtesting via Kupiec’s Proportion of Failures test indicates that jump-based, non-stationary models achieve statistically consistent risk coverage. These findings suggest discontinuous modeling as a prerequisite for regulatory-grade risk estimation in high-beta crypto assets.
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