Hierarchical Risk Parity (HRP) Allocation - Quant Researcher Interview Question
Difficulty: Hard
Category: machine_learning
Asked at: AHL, AllianceBernstein, Citadel, Two Sigma, Acadian Asset, BlackRock, Lazard Asset, AQR Capital Management, Man Group
Topics: finance, clustering, risk-parity, portfolio-optimization, machine-learning
Problem Description
Hierarchical Risk Parity (HRP) is a portfolio optimization technique that addresses the instability of quadratic optimizers by utilizing graph theory and hierarchical clustering to handle correlated assets. Unlike traditional mean-variance optimization, HRP does not require the inversion of a covariance matrix, making it robust to multicollinearity and noise in financial data. This method constructs a diversified portfolio by organizing assets into a tree structure and recursively allocating cap
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