Equity Option Prices and Firm Characteristics

We investigate the information content of firm characteristics for the cross-section of equity option prices. We first show that 42 out of 86 characteristics significantly explain differences in the implied volatility surface (IVS) across stocks. Then, we exploit this relation to price equity options. We estimate the IVS of a given stock by using machine learning to optimally pool option information from stocks with similar characteristics, which overcomes the illiquidity of the equity option market. Our method improves upon stock-specific benchmarks in pricing options out-of-sample and allows us to uncover the nonlinear interactions between characteristics and option prices.