Ledoit-Wolf covariance matrix estimator of stock returns
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Updated
Aug 23, 2019 - Python
Ledoit-Wolf covariance matrix estimator of stock returns
From-scratch mean-variance portfolio optimization toolkit reproducing canonical literature results.
Estimation error in portfolio construction — Ledoit-Wolf shrinkage from the paper, walk-forward horserace vs 1/N, and a 2×2 VaR backtest with Kupiec, Christoffersen and Basel traffic-light tests
Python library for mean-variance portfolio optimization — Black-Litterman returns, Ledoit-Wolf covariance, efficient frontier, risk parity, CVaR minimization, and walk-forward backtesting with transaction costs.
SIM Swap Fraud Prevention via Behavioral Biometrics
Unsupervised market-structure analytics - shrinkage/RMT-filtered correlation networks, minimum spanning trees, and a PCA systemic-risk index - plus a pre-registered research program that found no tradeable edge.
Robust portfolio construction using Ledoit-Wolf covariance shrinkage and Hierarchical Risk Parity (HRP) for stable, risk-aware asset allocation
Interactive S&P 500 portfolio allocation app — Markowitz framework, CVXPY optimization, Ledoit-Wolf covariance, Streamlit UI
A modular Python framework for covariance estimation, portfolio optimization, and rolling out-of-sample backtesting.
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