Robust Portfolio Optimization under Systematic Market Disruptions (PFSE)
This project introduces the Parallel Factor Space Estimator (PFSE), a hybrid framework for robust covariance estimation that resolves a fundamental trade-off in institutional portfolio management: traditional robust estimators (MCD, Tyler) offer strong statistical guarantees but are computationally infeasible for daily rebalancing of 100+ assets, while efficient methods (Ledoit-Wolf, sample covariance) have zero breakdown point against systematic contamination.
PFSE exploits a structural insight: during systematic market disruptions — flash crashes, monetary policy shocks, crisis contagion — extreme movements propagate through common factors, not idiosyncratic components. By concentrating robust estimation in reduced k-dimensional factor space (k=5 versus p=100–1000), PFSE inherits 25% breakdown point from MCD while achieving 32× computational speedup.
Work with Alessio Farcomeni (University of Roma Tor Vergata). Submitted to Computational Economics (Springer), March 2026.
Key Results
Validated through three complementary stages:
- Monte Carlo (p=100, ε=10% contamination): PFSE Sharpe 1.42 vs 0.96 for sample covariance — maintains 97% of clean-data performance while sample covariance degrades 31%
- S&P 500 backtest (2015–2025): Out-of-sample Sharpe 1.87 vs 1.63 (+14.7%), max drawdown −24.3% vs −34.1% (−29%) during COVID-19, turnover −42%
- Five stress scenarios: PFSE rank-1 across all scenarios, average Sharpe 1.67 vs 1.39 (+20%), lowest performance variability (CoV 0.041 vs 0.064)
- Economic value: $72M normal-period + $93M stress-period benefits per $1B portfolio, benefit-cost ratio 31:1
Interactive Explorer
The interactive app lets you explore the full results: run the PFSE estimation algorithm live, compare methods under increasing contamination levels, examine computational scalability, and inspect S&P 500 cumulative returns across all four market regimes.
