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Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret with Infinite Variance
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research explores minimax regret in contextual bilateral trade with infinite variance valuations, extending self-bounding properties to real-valued data.
OneBench interpretation
Institutional assessment
So what
This theoretical work on robust learning in heavy-tailed market conditions offers a foundation for more resilient trading and pricing algorithms.
Do what
Your quantitative research teams should monitor advances in robust contextual bandits for potential future application in high-volatility trading scenarios.