4 papers in the archive (2024–2026), average empirical rigor 6.9/10, average math complexity 6.1/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1, Street Traders ×1.
Works mostly in Risk Management & Tail Risk, Crypto Markets & DeFi, High-Frequency Trading & Optimal Execution, Market Microstructure.
Frequent co-authors: Fabrizio Lillo (3), Piero Mazzarisi (2), Daniele Maria Di Nosse (1), Sebastian Jaimungal (1), Manuel Naviglio (1), Francesco Tarantelli (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%).
In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR’s limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by providing the conditional expectation of such exceed
We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, wh
Decentralized Exchanges (DEXs) are now a significant component of the financial world where billions of dollars are traded daily. Differently from traditional markets, which are typically based on Limit Order Books, DEXs typically work as Automated Market Makers, and, since the implementation of Uni
Reputation is a fundamental mechanism through which markets sustain trust when service quality cannot be perfectly assessed ex ante, constituting a form of intertemporal economic capital by attracting future demand. Its effectiveness as a disciplinary mechanism depends not only on past interactions