Towards Data-Driven Volatility Modeling with Variational Autoencoders

Abstract

AbstractIn this study, we show how S &P 500 Index volatility surfaces can be modeled in a purely data-driven way using variational autoencoders. The approach autonomously learns concepts such as the volatility level, smile, and term structure without leaning on hypotheses from traditional volatility modeling techniques. In addition to introducing notable improvements to an existing variational autoencoder approach for the reconstruction of both complete and incomplete volatility surfaces, we showcase three practical use cases to highlight the relevance of this approach to the financial industry. First, we show how the latent space learned by the variational autoencoder can be used to produce synthetic yet realistic volatility surfaces. Second, we demonstrate how entire sequences of synthetic volatility surfaces can be generated to stress test and analyze an options portfolio. Third and last, we detect anomalous surfaces in our options dataset and pinpoint exactly which subareas are divergent. KeywordsVolatility modelingVariational autoencoderSynthetic dataMarket simulationAnomaly detectionPortfolio stress testing