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A deep dive into the multi-factor risk model that powers TheQuantHackers' automated position sizing and portfolio construction.
Every quant system needs a robust risk engine. Ours evaluates positions across five dimensions: market beta, sector concentration, volatility regime, correlation breakdown, and tail risk exposure.
We use a hierarchical risk parity (HRP) framework as the base layer, augmented with regime-detection overlays. When volatility clusters spike, the engine automatically shifts toward minimum-variance allocations.
Correlation Matrix Estimation: We use exponential weighting with a 63-day half-life, filtered through a random matrix theory (RMT) denoiser to remove noise eigenvalues.
Risk Decomposition: Each position's marginal contribution to risk is computed via Euler decomposition, giving us position-level risk budgets.
Regime Detection: A hidden Markov model classifies markets into low/medium/high volatility states. Allocation rules differ across regimes.
Backtested across 2019-2025, the risk engine reduced maximum drawdown by 34% compared to equal-weight allocation while maintaining 85% of the return profile.
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