Add to ORKGWe present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets withi...
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market...
We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging ...
This thesis proposes an application of state-of-the-art continuous action reinforcement learning for...
In many sequential decision-making problems we may want to manage risk by minimizing some measure of...
In the past decade, the application of deep reinforcement learning (DRL) in portfolio management has...
Deep reinforcement learning is recognised as an advantageous solution to automated stock trading. It...
Market exposed assets like stocks yield higher return than cash but have higher risk, while cash-equ...
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems u...
As a fundamental problem in algorithmic trading, portfolio optimization aims to maximize the cumulat...
We devise a hierarchical decision-making architecture for portfolio optimization on multiple markets...
This paper proposes a two-phase deep reinforcement learning approach for hedging variable annuities,...
The increasing complexity and dynamical property in stock markets are key challenges of the financia...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets withi...
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market...
We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging ...
This thesis proposes an application of state-of-the-art continuous action reinforcement learning for...
In many sequential decision-making problems we may want to manage risk by minimizing some measure of...
In the past decade, the application of deep reinforcement learning (DRL) in portfolio management has...
Deep reinforcement learning is recognised as an advantageous solution to automated stock trading. It...
Market exposed assets like stocks yield higher return than cash but have higher risk, while cash-equ...
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems u...
As a fundamental problem in algorithmic trading, portfolio optimization aims to maximize the cumulat...
We devise a hierarchical decision-making architecture for portfolio optimization on multiple markets...
This paper proposes a two-phase deep reinforcement learning approach for hedging variable annuities,...
The increasing complexity and dynamical property in stock markets are key challenges of the financia...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets withi...