3 papers in the archive (2023), average empirical rigor 3/10, average math complexity 7.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Volatility Modeling & Forecasting, Risk Management & Tail Risk, Commodities & Energy Markets, Stochastic Control & Optimal Stopping.
Frequent co-authors: Minglian Lin (2), William Wilson (1), Humayra Shoshi (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%).
Value at Risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a spe
Most of the existing methods for pricing Asian options are less efficient in the limit of small maturities and small volatilities. In this paper, we use the large deviations theory for the analysis of short-maturity Asian options. We present a local volatility model for the underlying market that in
In this paper, we consider the portfolio optimization problem in a financial market where the underlying stochastic volatility model is driven by n-dimensional Brownian motions. At first, we derive a Hamilton-Jacobi-Bellman equation including the correlations among the standard Brownian motions. We