3 papers in the archive (2025–2026), average empirical rigor 3.8/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Volatility Modeling & Forecasting, Statistical Arbitrage & Pairs Trading, Risk Management & Tail Risk.
Frequent co-authors: Siqi Shao (3), Arshia Ghasemi (2), Hamed Farahani (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%).
We analyze historic S&P500 multi-day returns: from daily returns to those accumulated over up to ten days. Despite symmetry breaking between gains and losses in the distribution of returns, resulting in its positive mean and negative skew, realized variance (volatility squared) exhibits remarkably g
We argue that negative skew and positive mean of the distribution of stock returns are largely due to the broken symmetry of stochastic volatility governing gains and losses. Starting with stochastic differential equations for stock returns and for stochastic volatility we argue that the distributio
We introduce a mean-reverting stochastic differential equation with a three-component stochastic term and show that it generates a hierarchy of steady-state (stationary) distributions. At the top level, the hierarchy is described by a modified-Beta distribution, while one- and two-parameter reductio