3 papers in the archive (2024–2025), average empirical rigor 7/10, average math complexity 5.3/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Volatility Modeling & Forecasting, Insurance & Actuarial Risk, Commodities & Energy Markets, Options & Derivatives Pricing.
Frequent co-authors: Bikramaditya Datta (1), Shiv Ratan Tiwari (1), Sudeep Shukla (1), Abbinav Sankar Kailasam (1), Anish Rai (1), Sejal Garg (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 Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for
Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illi
This study examines how market risks impact the sustainability and performance of the New Pension System (NPS). NPS relies on defined contributions from both employees and employers to build a corpus during the employee’s service period. Upon retirement, employees use the corpus fund to sustain thei