4 papers in the archive (2024–2026), average empirical rigor 7/10, average math complexity 6.1/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1, Street Traders ×1.
Works mostly in Crypto Markets & DeFi, Options & Derivatives Pricing, Market Microstructure, Volatility Modeling & Forecasting.
Frequent co-authors: Bhaskar Krishnamachari (4), Zhenbang Feng (1), Giovanni Zaarour (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%).
Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic
USDC and USDT are the dominant stablecoins pegged to $1 with a total market capitalization of over $300B and rising. Stablecoins make dollar value globally accessible with secure transfer and settlement. Yet in practice, these stablecoins experience periods of stress and de-pegging from their $1
Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regim
In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, with a focus on mechanisms for price stabilization, which is exemplified by the well-known stablecoi