4 papers in the archive (2023–2025), average empirical rigor 6.9/10, average math complexity 7.9/10. Quadrant mix: Holy Grail ×4.
Works mostly in Portfolio Optimization, Risk Management & Tail Risk, Options & Derivatives Pricing, Volatility Modeling & Forecasting.
Frequent co-authors: Svetlozar T. Rachev (3), Ayush Jha (2), Frank J. Fabozzi (2), Thilini V. Mahanama (1), Svetlozar Rachev (1), Ali M. Jaffri (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 develops a unified framework that integrates behavioral distortions into rational portfolio optimization by extracting implied probability weighting functions (PWFs) from optimal portfolios modeled under Gaussian and Normal-Inverse-Gaussian (NIG) return distributions. Using DJIA constitue
This paper develops and estimates a multivariate affine GARCH(1,1) model with Normal Inverse Gaussian innovations that captures time-varying volatility, heavy tails, and dynamic correlation across asset returns. We generalize the Heston-Nandi framework to a multivariate setting and apply it to 30 Do
We explore credit risk pricing by modeling equity as a call option and debt as the difference between the firm’s asset value and a put option, following the structural framework of the Merton model. Our approach proceeds in two stages: first, we calibrate the asset volatility using the Black-Scholes
The financial industry should be involved in mitigating the risk of downturns in the financial wellbeing indices around the world by implementing well-developed financial tools such as insurance instruments on the underlying wellbeing indices. We define a new quantitative measure of the wellbeing of