3 papers in the archive (2024–2026), average empirical rigor 3.8/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Philosophers ×1.
Works mostly in Crypto Markets & DeFi, Machine Learning for Market Forecasting, Risk Management & Tail Risk, Volatility Modeling & Forecasting.
Frequent co-authors: Mohammad Jelodari Mamaghani (1), Parham Pajouhi (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%).
Extreme volatility, nonlinear dependencies, and systemic fragility are characteristics of cryptocurrency markets. The assumptions of normality and centralized control in traditional financial risk models frequently cause them to miss these changes. Four components-volatility stress testing, stableco
We suggest employing log-ergodic processes to simulate the velocity of money in an ergodic manner. Our approach sheds light on economic behavior, policy implications, and financial dynamics by maintaining long-term stability. By bridging theory and practice, the partially ergodic model helps analyst
Kladia Liquidity Deflator (KLD) is an XRPL-based, debt-indexed token whose supply dynamics respond directly to a debt index derived from macroeconomic data sources. The model links indebtedness to deterministic adjustments in issuance, burns, and escrow release caps, creating a rule-based deflationa