Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the...

We investigate the cross-sectional return predictability of delta-hedged equity options using machine learning and big data. Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample...

We derive risk-neutral option price formulas for plain-vanilla temperature futures derivatives on the basis of several multi-factor Ornstein-Uhlenbeck temperature models which allow for seasonality in the mean level and volatility. Our main innovation consists in an incorporation of omnipresent...

In this paper, we present a novel method to extract the risk-neutral probability of default from American put option prices. Under the assumptions of Carr and Wu (2011), we derive a closed form expression for American put options from which the probability of default can be inferred. Our...

We document that a theoretically founded, real-time, and easy-to-implement option-based measure, termed synthetic-stock difference (SSD), accurately estimates the part of stock's expected return arising from stock's transaction costs. We calculate SSD for U.S. optionable stocks. SSD can be more...

We apply a two-step strategy to forecast the dynamics of the volatility surface implicit in option prices to all American-style options written on the stocks that have entered the Dow Jones Industrial Average Index between 2004 and 2016. We explore whether the implied volatilities extracted...

This study compares the performances of neural network and Black-Scholes models in pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting approaches. Since the volatility is the key parameter in pricing options, GARCH (Generalized Autoregressive...

We calibrate neural stochastic differential equations jointly to S&P 500 smiles, VIX futures, and VIX smiles. Drifts and volatilities are modeled as neural networks. Minimizing a suitable loss allows us to fit market data for multiple S&P 500 and VIX maturities. A one-factor Markovian stochastic...

We introduce an asymptotic expansion for forward start options in a multi-factor local-stochastic volatility model. We derive explicit approximation formulas for the so-called forward implied volatility which can be useful to price complex path-dependent options, as cliquets. The expansion...

We perform a comprehensive Monte Carlo comparison between nine procedures available in the literature to detect jumps in financial assets proposed by Barndorff-Nielsen and Shephard (2006), Andersen et al. (2007), Lee and Mykland (2008), A¨ıt-Sahalia and Jacod (2008), Jiang and Oomen (2008),...