The increasing penetration of renewable generations, along with the deregulation and
marketization of power industry, promotes the transformation of energy market operation …

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the
returns. A risk measure often focuses on the worst returns out of the agent's experience. As a …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

This paper reviews 17 studies addressing dynamic option hedging in frictional markets
through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL …

We 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 …

Reinforcement Learning (RL) has experienced significant advancement over the past
decade, prompting a growing interest in applications within finance. This survey critically …

This work considers the problem of a trader who must manage the Credit Valuation
Adjustment (CVA) of a derivative, defined as the risk-neutral expectation of losses incurred if …

In this paper, we focus on finding the optimal hedging strategy of a credit index option using
reinforcement learning. We take a practical approach, where the focus is on realism ie …

Reinforcement learning has proven to be successful in obtaining profitable trading policies;
however, the effectiveness of such strategies is strongly conditioned to market stationarity …

In the field of portfolio management using reinforcement learning, existing approaches have
mainly focused on cash-only trading, overlooking the potential benefits and risks of margin …