Risk-Parity Allocation Across Tokenized Assets and Bitcoin

Summary

The article describes a risk-parity strategy for a basket of tokenized traditional assets and BTC: an equity index proxy, gold, crude oil, and Bitcoin. Rather than assigning equal capital, it seeks roughly equal risk contributions, accounting for both asset volatility and correlation through a covariance matrix. Hourly price series are aligned across instruments, converted to log returns, and used to estimate exponentially weighted covariance with diagonal regularization. An iterative solver adjusts weight magnitudes while holding previously chosen directions fixed; it normalizes absolute weights and falls back to equal-weight longs if convergence fails.

The discussion explains parameter choices and assumptions, including a decay coefficient adopted from daily-data practice despite hourly inputs, simplified return centering, and fat-tailed crypto returns that covariance may understate. It also flags asynchronous trading hours, limited history, heuristic direction selection and rebalance settings, and live order sequencing that may open a new position before a close is confirmed. The article presents the implementation as an exploratory starting point, not a validated allocation strategy; paper trading cannot establish that its assumptions or live execution will hold.

Key ideas

• Risk parity targets balanced contributions to portfolio risk rather than equal capital weights.
• The covariance matrix captures both asset volatility and cross-asset relationships in the allocation.
• The described implementation estimates covariance with exponentially weighted hourly log returns and regularizes the matrix.
• The solver iteratively adjusts magnitudes while keeping chosen long or short directions fixed.
• Market-hour gaps, fat tails, limited data, heuristic parameters, and uncertain order fills constrain the method.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.