3 papers in the archive (2023–2024), average empirical rigor 4.3/10, average math complexity 5.3/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Philosophers ×1.
Works mostly in Volatility Modeling & Forecasting, Insurance & Actuarial Risk, Stochastic Control & Optimal Stopping, Portfolio Optimization.
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%).
Consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. At time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of withdrawals. Require annuitants to be homogeneous in the sense that their initial contributions a
Daily leveraged exchange traded funds amplify gains and losses of their underlying benchmark indexes on a daily basis. The result of going long in a daily leveraged ETF for more than one day is less clear. Here, bounds are given for the log-returns of a leveraged ETF when going long for more than ju
Over the past 60 years, there has been a gradual increase in the volatility of daily returns for the S&P 500 Index. Hypothetically, suppose that market forces determine daily volatility such that a daily leveraged S&P 500 fund cannot outperform a standard S&P 500 fund in the long run. Then this hypo