4 papers in the archive (2022–2024), average empirical rigor 5.9/10, average math complexity 7/10. Quadrant mix: Holy Grail ×4.
Works mostly in Volatility Modeling & Forecasting, Fixed Income & Interest Rates, Stochastic Control & Optimal Stopping, Options & Derivatives Pricing.
Frequent co-authors: Orcan Ogetbil (2), Ali Fathi (1), Arun Kumar Polala (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Differential machine learning (DML) is a recently proposed technique that uses samplewise state derivatives to regularize least square fits to learn conditional expectations of functionals of stochastic processes as functions of state variables. Exploiting the derivative information leads to fewer s
We formulate a forward inflation index model with multi-factor volatility structure featuring a parametric form that allows calibration to correlations between indices of different tenors observed in the market. Assuming the nominal interest rate follows a single factor Gaussian short rate model, we
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of transaction costs under discrete trading schedules. We
We propose a non-parametric extension with leverage functions to the Andersen commodity curve model. We calibrate this model to market data for WTI and NG including option skew at the standard maturities. While the model can be calibrated by an analytical formula for the deterministic rate case, the