4 papers in the archive (2024–2026), average empirical rigor 6/10, average math complexity 5.4/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1, Street Traders ×1.
Works mostly in Crypto Markets & DeFi, Options & Derivatives Pricing, Volatility Modeling & Forecasting, Market Microstructure.
Frequent co-authors: Kundan Mukhia (2), Md. Nurujjaman (2), Suman Saha (2), Chittaranjan Hens (2), SR Luwang (1), Agni Rakshit (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 study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Reque
Scaling laws offer a powerful lens to understand complex transactional behaviors in decentralized systems. This study reveals distinctive statistical signatures in the transactional dynamics of ERC20 tokens on the Ethereum blockchain by examining over 44 million token transfers between July 2017 and
The Black-Scholes option pricing model remains a cornerstone in financial mathematics, yet its application is often challenged by the need for accurate hedging strategies, especially in dynamic market environments. This paper presents a rigorous analysis of hedge errors within the Black-Scholes fram
Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-ter