Circular Image

A. Papapantoleon

23 records found

Effects of exploration-exploitation strategies in dynamic Forex markets

The use of Reinforcement Learning in Algorithmic Trading

This paper examines how different exploration strategies affect the learning behavior and trading performance of reinforcement learning (RL) agents in a custom foreign exchange (forex) environment. By holding all other components constant—including model architecture, features, a ...

Feature Engineering in Reinforcement Learning for Algorithmic Trading

Investigating the Effects of State Representation on Trading Agent Performance in the Forex Market

This study explores how different features impact a Reinforcement Learning agent's performance in forex trading. Using a Deep Q-Network (DQN) agent and EUR/USD data from 2022-2024, we found that performance is highly sensitive to the information provided. Key findings show that f ...

The use of Reinforcement Learning in Algorithmic Trading

What are the impacts of different possible reward functions on the ability of the RL model to learn, and the performance of the RL Model?

Algorithmic trading already dominates modern financial markets, yet most live systems still rely on fixed heuristics that falter when conditions change. Deep reinforcement learning agents promise adaptive decision making, but their behaviour is driven entirely by the reward funct ...

Transferable Reinforcement Learning in Forex Trading

Cross-Currency Adaptation Techniques for EUR/USD and GBP/USD

This paper investigates the effectiveness of transfer learning techniques for accelerating the training of deep reinforcement learning (RL) agents in the foreign exchange (Forex) market. Specifically, the transfer of policies learned on the EUR/USD currency pair to the GBP/USD pa ...
Green hydrogen is increasingly recognised as a key enabler of the energy transition, offering a carbon-free alternative for hard-to-abate sectors. However, its large-scale deployment remains economically challenging due to high capital expenditures (CAPEX) and production costs. T ...
Rough volatility models have become a prominent tool in quantitative finance due to their ability to cap- ture the rough nature of financial time series. However, these models typically have a non-Markovian structure, and this poses significant computational challenges. Existing ...
Electronic trading algorithms are at the centre of every buy-side equity trading desk. These algorithms rely often on market impact models, which are stochastic models for the stock prices that account for the feedback effects of trading. Propagator models are central tools for d ...

A Novel Approach to FX Swap Portfolio Management

With an Application in Portfolio Optimization

In this thesis, we define a new concept of duration for FX Swaps and more broadly for sovereign bonds. The con-cept of duration already exists for bonds and more specifically coupon bonds, where it is also called ”Macauley Duration”. We aim to define a concept for FX Swaps with s ...

Market Making in Limit Order Books

Using Reinforcement Learning

Market making, the act of providing liquidity to the market by simultaneously buying and selling, is a difficult problem to solve. The use of reinforcement learning to solve for market making is increasing, as academics and practitioners alike look for novel ways to approximate f ...
This research project, conducted in collaboration between TU Delft and MN, a pension fund asset manager, focuses on the optimal venue selection in FX trading. The objective is to investigate how the venue selection affects trading performance and to improve MN trading execution a ...

(Dynamic) hedging of a mortgage portfolio

Investigating margin and value stability

Banks issue mortgages with an embedded option for borrowers to prepay a part of the loan. However, this behaviour poses a risk to banks as it disrupts the level and timing of mortgage cash flows. From an earning perspective, when interest rates decrease, customers are financially ...
This thesis investigates the application of neural stochastic differential equations (NSDEs) in financial modeling. It begins by presenting existing theoretical interpretation of NSDEs and investigates the properties of their solutions. By establishing a solid foundation, the t ...
The increasing number of Renewable Sources (RES) in the European electric grid has resulted in the necessity for producers to adjust their position with respect to the change in weather forecasting. Therefore, the European Power Exchange (EPEX SPOT) has seen an expansion of the I ...
This thesis presents a comprehensive exploration of the rough Heston model as a means to enhance financial derivative pricing and calibration in the context of the complex behavior of market volatility. Recognizing the limitations of classical models, such as the Black-Scholes an ...
In this research, we consider neural network-algorithms for option pricing. We use the Black-Scholes model and the lifted Heston model. We derive the option pricing partial differential equation (PDE), which we solve with a neural network, and the conditional characteristic funct ...

Option Pricing Techniques

Using Neural Networks

With the emergence of more complex option pricing models, the demand for fast and accurate numerical pricing techniques is increasing. Due to a growing amount of accessible computational power, neural networks have become a feasible numerical method for approximating solutions to ...
This thesis investigates the application of machine learning models on foreign exchange data around the WM/R 4pm Closing Spot Rate (colloquially known as the WMR Fix). Due to the nature of the market dynamics around the WMR Fix, inefficiencies can occur and therefore some predict ...
The right to use a certain amount of capacity in an electrical cable between two countries for the purpose of trading energy is an asset that can be bought. Each hour of capacity can be seen as a real spread option with the energy prices of each country being the underlying proce ...
Since the introduction of rough volatility there have been numerous attempts at combining it with existing models in order to better approximate the volatility surface with a low number of parameters. The drawback of rough volatility is usually the time needed to compute a volati ...