Transformers versus LSTMs for electronic trading

1 code implementation • SSRN 2023 • Paul Bilokon, Yitao Qiu

Therefore, the question this study wants to answer is: whether the Transformer-based model can be applied in financial time series prediction and beat LSTM.

Time Series Time Series Forecasting +1

Derivatives Sensitivities Computation under Heston Model on GPU

1 code implementation • SSRN 2023 • Pierre-Antoine Arsaguet, Paul Bilokon

Our results show that the proposed method provides a speed-up up to 200x compared to the exact simulation implementation and that it can be used for both European and Asian options.

Applying Deep Learning to Calibrate Stochastic Volatility Models

1 code implementation • 14 Sep 2023 • Abir Sridi, Paul Bilokon

In this work, we also introduce different regularisation techniques, and we apply them notably in the case of the DML.

From Deep Filtering to Deep Econometrics

no code implementations • 13 Sep 2023 • Robert Stok, Paul Bilokon

Calculating true volatility is an essential task for option pricing and risk management.

Econometrics Management

Real-time VaR Calculations for Crypto Derivatives in kdb+/q

no code implementations • 11 Sep 2023 • Yutong Chen, Paul Bilokon, Conan Hales, Laura Kerr

Cryptocurrency market is known for exhibiting significantly higher volatility than traditional asset classes.

Time Series

A compendium of data sources for data science, machine learning, and artificial intelligence

no code implementations • 10 Sep 2023 • Paul Bilokon, Oleksandr Bilokon, Saeed Amen

Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models.

Drug Discovery

Exploring the Advantages of Transformers for High-Frequency Trading

1 code implementation • 20 Feb 2023 • Fazl Barez, Paul Bilokon, Arthur Gervais, Nikita Lisitsyn

This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models.

Decoder Position +3

Quasi-Monte Carlo methods for calculating derivatives sensitivities on the GPU

1 code implementation • SSRN 2022 • Paul Bilokon, Sergei Kucherenko, Casey Williams

Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions.

Management

Iterated and exponentially weighted moving principal component analysis

1 code implementation • SSRN 2021 • Paul Bilokon, David Finkelstein

The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance.

An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication

no code implementations • 5 Jul 2021 • Méabh MacMahon, Woochang Hwang, Soorin Yim, Eoghan MacMahon, Alexandre Abraham, Justin Barton, Mukunthan Tharmakulasingam, Paul Bilokon, Vasanthi Priyadarshini Gaddi, Namshik Han

Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases.

Action Analysis

Market regime classification with signatures

1 code implementation • 30 Jun 2021 • Paul Bilokon, Antoine Jacquier, Conor McIndoe

We provide a data-driven algorithm to classify market regimes for time series.

Classification Clustering +2