Know the asset's fundamental value, then model its market and manage the risk.

I combine quantitative credit modeling with practical underwriting experience, including company screening, financial analysis, term-sheet evaluation, and debt-schedule modeling. My MFE work extends this through structural and statistical default models to estimate default probability, distance-to-default, and recovery risk.

My fixed-income work focuses on modeling interest-rate dynamics, valuation, and relative-value strategies. I have calibrated BDT trees to observed yield curves, valued bonds and embedded options, and constructed DV01-neutral Treasury curve strategies to isolate and manage yield-curve risk.

I evaluate portfolio risk across market regimes using VaR, CVaR, GARCH, EWMA, stress testing, and Extreme Value Theory, with an emphasis on backtesting models against realized losses. My work also examines hedging effectiveness, volatility risk, and how changing correlations and market conditions affect portfolio downside.

I use factor models, PCA, clustering, and portfolio optimization to identify return drivers, measure exposures, and construct portfolios around risk and return objectives. My work focuses on diversification, factor and correlation structure, portfolio constraints, and evaluating whether investment signals remain robust out of sample.

I apply time-series modeling, Monte Carlo simulation, machine learning, and numerical optimization to investment and risk problems. I use these methods to model market dynamics, identify predictive relationships, estimate downside risk, and test the robustness of investment and credit models.