4 papers in the archive (2022–2026), average empirical rigor 5/10, average math complexity 6/10. Quadrant mix: Lab Rats ×2, Street Traders ×2.
Works mostly in Volatility Modeling & Forecasting, Market Microstructure, Fixed Income & Interest Rates, Portfolio Optimization.
Frequent co-authors: Nikhil Devanathan (2), Dylan Rueter (2), Trevor Hastie (2), Emmanuel Candès (1), Mykel J. Kochenderfer (1), Arpit Apoorv (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%).
This paper proposes a simulation-based framework for assessing and improving the performance of a pension fund management scheme. This framework is modular and allows the definition of customized performance metrics that are used to assess and iteratively improve asset and liability management polic
This paper introduces a methodology for constructing a market index composed of a liquid risky asset and a liquid risk-free asset that achieves a fixed target volatility. Existing volatility-targeting strategies typically scale portfolio exposure inversely with a variance forecast, but such open-loo
Funds at large portfolio management firms may consist of many portfolio managers (PMs), each managing a portion of the fund and optimizing a distinct objective. Although the PMs determine their trades independently, the trade lists may be netted and executed by the firm. These net trades may be suff
The minimum (worst case) value of a long-only portfolio of bonds, over a convex set of yield curves and spreads, can be estimated by its sensitivities to the points on the yield curve. We show that sensitivity based estimates are conservative, \ie, underestimate the worst case value, and that the ex