3 papers in the archive (2025–2026), average empirical rigor 7.7/10, average math complexity 6.2/10. Quadrant mix: Holy Grail ×3.
Works mostly in Commodities & Energy Markets, Volatility Modeling & Forecasting, Options & Derivatives Pricing, Market Microstructure.
Frequent co-authors: Salam Rabindrajit Luwang (2), Buddha Nath Sharma (2), Kundan Mukhia (2), Md. Nurujjaman (2), Filippo Petroni (2), Sourish Das (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%).
Quantitative understanding of stochastic dynamics in limit order price changes is essential for execution strategy design. We analyze intraday transition dynamics of ask and bid orders across market capitalization tiers using high-frequency NASDAQ100 tick data. Employing a discrete-time Markov chain
Financial markets alternate between tranquil periods and episodes of stress, and return dynamics can change substantially across these regimes. We study regime-dependent dynamics in developed and developing equity indices using a data-driven Hilbert–Huang-based regime identification and profiling pi
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