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Found 11 papers, 0 papers with code
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in a unified manner.
Collective Arbitrage and the Value of Cooperation
We introduce the notions of Collective Arbitrage and of Collective Super-replication in a setting where agents are investing in their markets and are allowed to cooperate through exchanges.
Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms
In this work we propose deep learning-based algorithms for the computation of systemic shortfall risk measures defined via multivariate utility functions.
Deep Learning for Systemic Risk Measures
The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations.
Systemic Risk Models for Disjoint and Overlapping Groups with Equilibrium Strategies
We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor.
Reinforcement Learning for Mean Field Games, with Applications to Economics
Mean field games (MFG) and mean field control problems (MFC) are frameworks to study Nash equilibria or social optima in games with a continuum of agents.
Sub- and Super-solution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Market
The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors.
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems
We present a Reinforcement Learning (RL) algorithm to solve infinite horizon asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems.
Optimal Investment with Correlated Stochastic Volatility Factors
The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention.
Multiscale Asymptotic Analysis for Portfolio Optimization under Stochastic Environment
This completes the analysis of portfolio optimization in both fast mean-reverting and slowly-varying Markovian stochastic environments.
