Projects / Case Studies
The case studies below illustrate typical analytical deliverables and workflows. They are presented as examples and can be adapted to specific datasets and objectives.
Case Study 1: Multi-Asset Portfolio Diversification Diagnostics
Objective
Assess diversification quality and risk concentration within a multi-asset portfolio.
Data Used
Historical prices and returns for a diversified set of asset classes and a benchmark reference.
Methodology
Time-series preprocessing, return computation, correlation analysis, and risk contribution diagnostics to identify concentration drivers.
Key Insights and Outcomes
Identification of correlation clusters, dominant risk exposures, and diversification gaps, with a documented set of analytical recommendations for improved allocation robustness (decision-support oriented).
Case Study 2: Portfolio Risk and Drawdown Assessment
Objective
Quantify downside risk and evaluate portfolio behavior under adverse market conditions.
Data Used
Daily portfolio returns over multiple periods including stress regimes; benchmark returns for comparison.
Methodology
Volatility and drawdown computation, Value-at-Risk estimation, and historical scenario analysis to evaluate tail-risk sensitivity.
Key Insights and Outcomes
Clear mapping of drawdown episodes, risk metric interpretation aligned with empirical outcomes, and structured reporting suitable for monitoring.
Case Study 3: Rule-Based Rebalancing Analytics and Monitoring
Objective
Design a transparent rebalancing framework to control allocation drift and stabilize portfolio risk exposure.
Data Used
Target weights, asset returns, and time-series volatility and correlation estimates.
Methodology
Drift measurement, threshold design, rebalancing frequency comparison, and performance evaluation against a benchmark and a no-rebalancing baseline.
Key Insights and Outcomes
Quantified impact of rebalancing rules on risk and tracking behavior, with reproducible reporting and documented assumptions.
Notes on Reproducibility
Each project can be delivered with documented methodology, reproducible calculations, and Python-based notebooks or scripts, depending on client needs and context.