Can you use autoregressive diffusion to generate market data?
This article explores the application of autoregressive diffusion models to generate synthetic market data for quantitative finance. It discusses the challenges of modeling market events, which possess both discrete and continuous characteristics.
Why it matters
Advancements in generative modeling for financial data could revolutionize how firms test trading strategies and predict market behavior.
The following is part of a series of posts about 2026 summer intern projects – for more, see “What the interns have wrought, special jumbo 2026 edition”
In quantitative finance we are used to models that take a stream of market data events for a given symbol (like resting orders being added to an order book, cancellations of those orders, executions) and predict that symbol’s future price. Generative models are less common. Imagine a model that could give not just a point estimate of a symbol’s price, but could actually synthesize book events—including the timing of their arrival on the exchange. You’d get price predictions, of course, but your rollouts would have hugely more texture than just that.
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