2 citations · 2 across the 5 of their papers we have counts for
6 papers
Randomized Neural Networks for estimation of exposure profiles and Credit Valuation Adjustment (CVA) for American Equity Options
Isidro Moroso Varona, Jakub Michańków, Paweł Sakowski
This paper studies the use of randomized neural networks for the estimation of exposure profiles and unilateral CVA of American options within a Monte Carlo framework. The analysis…
Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
Zofia Bracha, Paweł Sakowski, Jakub Michańków
This paper explores the application of deep Q-learning to hedging at-the-money options on the S\&P~500 index. We develop an agent based on the Twin Delayed Deep Deterministic Polic…
Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
Jakub Michańków
This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) a…
Alternative Loss Function in Evaluation of Transformer Models
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
The proper design and architecture of testing machine learning models, especially in their application to quantitative finance problems, is crucial. The most important aspect of th…
Generalized Mean Absolute Directional Loss for Machine Learning Trading Models
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
The article presents and evaluates a custom loss function designed specifically for machine learning models used in algorithmic trading. Regardless of the selected asset class and…
Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of al…