no code implementations • 27 Dec 2023 • Erdinc Akyildirim, Matteo Gambara, Josef Teichmann, Syang Zhou
We present convincing empirical results on the application of Randomized Signature Methods for non-linear, non-parametric drift estimation for a multi-variate financial market.
no code implementations • 28 Nov 2023 • Matteo Gambara, Giulia Livieri, Andrea Pallavicini
In the present work, we introduce and compare state-of-the-art algorithms, that are now classified under the name of machine learning, to price Asian and look-back products with early-termination features.
no code implementations • 7 Jan 2022 • Erdinc Akyildirim, Matteo Gambara, Josef Teichmann, Syang Zhou
In this case, we are able to identify pump and dump attempts organized on social networks with F1 scores up to 88% by means of our unsupervised learning algorithm, thus achieving results that are close to the state-of-the-art in the field based on supervised learning.
no code implementations • 16 Jun 2020 • Matteo Gambara, Josef Teichmann
Consistent Recalibration models (CRC) have been introduced to capture in necessary generality the dynamic features of term structures of derivatives' prices.
no code implementations • 17 Jan 2019 • Dominik Alfke, Weston Baines, Jan Blechschmidt, Mauricio J. del Razo Sarmina, Amnon Drory, Dennis Elbrächter, Nando Farchmin, Matteo Gambara, Silke Glas, Philipp Grohs, Peter Hinz, Danijel Kivaranovic, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan Macdonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.