Institutional Portfolio
Intelligence.
DCC-GARCH dynamic correlations, Hierarchical Risk Parity weights, and Ledoit-Wolf shrinkage — delivered as an institutional PDF memo.
$299 per report. Delivered in 24 hours.
A 4-section institutional memo. No filler.
Executive Summary
Key risk findings. The single most important insight from your portfolio data. Recommended action in 3 sentences.
Risk Analysis
Portfolio volatility vs benchmarks. Sharpe ratio. DCC-GARCH stress correlations — what happens to your correlations during a selloff.
HRP Portfolio Construction
Optimal weights from Hierarchical Risk Parity (Lopez de Prado 2016). How they differ from your current allocations and why.
Factor Exposures & Next Steps
Beta, alpha, annualized vol per position. 3 specific, actionable next steps you should take within 30 days.
The math is peer-reviewed. Not invented here.
Ledoit-Wolf Shrinkage
Ledoit & Wolf, 2004Analytical shrinkage of the sample covariance toward scaled identity. Solves the ill-conditioning problem in high-dimensional covariance estimation. Better portfolio optimization than raw sample covariance.
DCC-GARCH(1,1)
Engle, 2002Two-step: fit GARCH(1,1) to each return series → DCC(1,1) on standardized residuals. Captures time-varying correlations. Key finding: correlations spike during stress — knowing this prevents underestimating portfolio tail risk.
Hierarchical Risk Parity
Lopez de Prado, 2016Converts covariance to correlation distance matrix → hierarchical clustering → quasi-diagonalization → recursive bisection. Allocates inversely proportional to sub-portfolio variance. Out-of-sample diversification outperforms naive 1/N and Markowitz.
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