Generative ML methods are becoming more popular for use cases like imputation of financial data, as well as forecasting asset prices. Generative models are especially suited for estimating point values and uncertainty, giving us a measure of confidence in estimation.
In this talk, we will cover topics like:
Collaborative filtering methodologies for illiquid asset pricing, using generative deep gaussian processes to estimate behavior of a target instrument from the observations on more liquid, closely-related instruments
Machine learning techniques for financial data imputation, e.g., estimating companies' granular geographical exposure given sparse & aggregate disclosure in financial statements
