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Market Generators: A Paradigm Shift in Financial Modeling

In: Signature Methods in Finance

Author

Listed:
  • Blanka Horvath

    (Mathematical Institute and Oxford Man Institute of Quantitative Finance)

  • Jonathan Plenk

    (Mathematical Institute and Oxford Man Institute of Quantitative Finance)

  • Milena Vuletić

    (Mathematical Institute and Oxford Man Institute of Quantitative Finance)

Abstract

Market Generators are a rapidly evolving class of neural-network-based models to simulate financial market behavior, offering a powerful alternative to classical stochastic models. These deep learning models are trained to encode the underlying distribution of financial data and generate new synthetic market scenarios from the learned distribution. Though the expressions “Market Generator” and its related form “Market Simulator” have only entered the vocabulary of financial modeling around 2019, by today, the modeling techniques related to them have already grown into an area in their own right. This growing interest is matched by a dramatic rise in research, publications and an accelerating rate of innovation in the ambient technological arena of generative modeling. Recently, this trend has culminated in the emergence of large generative networks, particularly GPT-type language models, which are proving to be driving one of the biggest disruptions in the history of technology.

Suggested Citation

  • Blanka Horvath & Jonathan Plenk & Milena Vuletić, 2026. "Market Generators: A Paradigm Shift in Financial Modeling," Springer Finance, in: Christian Bayer & Goncalo dos Reis & Blanka Horvath & Harald Oberhauser (ed.), Signature Methods in Finance, pages 127-159, Springer.
    • Handle: RePEc:spr:sprfcp:978-3-031-97239-3_4
    DOI: 10.1007/978-3-031-97239-3_4
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