Data-driven Hedging of Stock Index Options via Deep Learning

Options hedging is an important problem in financial markets. The prevailing approach to hedging first assumes a parametric stochastic model for the dynamics of the underlying asset. The model is then calibrated to observed option prices from the market, based on which various sensitivities are computed and used to hedge the risk of options. Popular choices include local volatility models ([5]), stochastic volatility models ([15], [12], [8]), jump-diffusions and purejump processes ([4], [18], [20]). Despite the prevalence of the model-based approach, it is well understood that model risk can affect the hedging result significantly. Recently, a data-driven approach that doesn't rely on any stochastic model for the underlying asset is proposed.