Monte-Carlo Portfolio Optimization under Non-Normal Returns
Seed: asset_return_distributions, investor_utility, constraints, transaction_costsADVERTISEMENT - IN-ARTICLE
Implementation Guide
A Monte-Carlo optimizer that accepts fat-tailed return distributions, investor utility functions (mean-variance, CVaR, prospect-theory) and realistic constraints to produce allocations robust to non-normal risks. Useful for quant PMs managing tail-sensitive portfolios seeking improved downside control.
💡 Expert Q&A Insights
Q: How computationally heavy is this?
Monte Carlo can be compute-intensive; use variance reduction and parallelization. \n
Q: How to explain results to stakeholders?
Provide scenario visualizations and probability-of-loss measures rather than raw math.