A novel generative machine learning approach for the simulation of sequences of financial price data with drawdowns quantifiably close to empirical data is introduced. Applications such as pricing drawdown insurance options or developing portfolio drawdown control strategies call for a...

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Published on March 01, 2023 | 1 min read

Data-driven portfolio construction is non-parametric in the sense that it does not impose its objective on a parametric representation of input paths, such as a variance-covariance matrix, but rather imposes its objective directly on these paths without having to make assumptions on th...

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Published on January 31, 2023 | 1 min read

This short paper was presented at the 2022 R/Finance conference in Chicago, IL. It picks one of the above-mentioned architectures - a CVAE - and introduces a signature-based drawdown reconstruction cost loss term. The result is a host of realistic drawdown scenarios, where the optimal portfoli...

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Published on June 03, 2022 | 0 min read

Market generators are generative machine learning (ML) models with the specificity of modeling financial markets - such as stocks’ spot markets, options’ vol surfaces and limit order books (market microstructure).The topic has seen a recent surge in interest, and this...

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Published on March 01, 2022 | 1 min read

My other projects hinge on the concepts of paths and path signatures as prerequisites. In the words of Terry Lyons1: The key idea behind signa...

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Published on November 01, 2021 | 1 min read