3 papers in the archive (2025), average empirical rigor 8/10, average math complexity 8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing, Fixed Income & Interest Rates, Machine Learning for Market Forecasting.
Frequent co-authors: A. Alexandre Trindade (1), Svetlozar T. Rachev (1), Svetlozar T Rachev (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 three-dimensional decomposition of volatility memory into orthogonal components of level, shape, and tempo. The framework unifies regime-switching, fractional-integration, and business-time approaches within a single canonical representation that identifies how each dimension g
Financial returns are known to exhibit heavy tails, volatility clustering and abrupt jumps that are poorly captured by classical diffusion models. Advances in machine learning have enabled highly flexible functional forms for conditional means and volatilities, yet few models deliver interpretable s
We extend the Lindquist-Rachev (LR) option-pricing framework–which values derivatives in markets lacking a traded risk-free bond–by introducing common Levy jump dynamics across two risky assets. The resulting endogenous “shadow” short rate replaces the usual risk-free yield and governs discounting