* Synthetic market data generation
Deep learning has recently had a major impact on several areas of quantitative finance, by making it possible to speed up calculations that have be run on a daily basis by several orders of magnitude, by allowing address investment problems in much greater generality than before and allowing us to do modelling under more realistic assumptions.
One of the most significant developments has been our ability to synthetically generate artificial market data, in particular, the simulation of highly realistic (past and future) paths of financial time series. This is dubbed as 'market generation' and 'market simulation'. Recent advances have shown that a particularly powerful method to do market simulation can be achieved by combining deep generative modelling with a signature-based encoding of the underlying paths.