Statistical Methods in Finance 2021

June 27 to July 1, 2021, 2-8pm IST


Model-Free Mean-Field Reinforcement Learning

By Rene Carmona
Princeton University, USA

We present an abstract measure theoretical framework for discrete time decision processes which can accommodate closed loop as well as open loop policies. The mean-field nature of the model is justified by a large number of controller limit. The conditional nature of the interactions is justified by the presence of idiosyncratic and common sources of noise. We derive a form of the dynamic programming principle for the state-action value function and a Q-learning algorithm for these new MDP models.

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Call for Papers in Sankhya B: Special Issue on Recent Advances in Statistical Finance

The aim of this special issue is to feature research papers on theory, methodology, and applications of models and methods for recent advances in statistical finance. We encourage submissions presenting original works on statistical, computational, and mathematical approaches to modelling and analysis of financial data. Innovative applications and case studies in financial statistics are welcome, especially related to novel methodological challenges in the treatment of big data and high-frequency data.

This special issue will bring together contributions from practitioners and researchers working on different aspects of statistical methods in finance, with methodological interests encompassing, but not limited to, the following domains:

The motivating application areas could be: For More Detail ...

If you are a student and want your paper to be considered for student paper competition, then ask your supervisor to send a mail at, with a particular mention that you were the primary contributor and author of the paper by May 15, 2021.

You must submit your paper by May 15, 2021, to be considered for the competition. Mail your paper at

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