Riskfuel Director of R&D Maxime Bergeron presents how Riskfuel is using variational autoencoders to fill in the missing information of implied volatility surfaces.
Implied volatility surfaces are ubiquitous in quantitative finance and many valuation tools use them as inputs. However, surfaces produced from market data are usually incomplete, so they need to be interpolated and extrapolated. In this talk, we will explain how variational autoencoders can remove human bias from this procedure and let the data speak for itself through unsupervised learning. The resulting light-weight models can be used to produce synthetic volatility surfaces that are indistinguishable from those observed in the market. This allows us to robustly complete partially observed volatility surfaces without making assumptions about the process driving the underlying asset or the shape of the surface.
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