In May of 1997, in the midst of the internet bubble, the average month end P/E ratio for the software industry was 44. However, the five year historical average was 31. In this study we examine the effect of this industry value fluctuation on the effects of option prices. We examine the...

I show that the inventory risk faced by market-makers has a first-order effect on option prices. I introduce a simple approach that decomposes the price impact of trades into inventory risk and asymmetric information components. While both components are large for option trades, the inventory...

The area of derivatives is arguably the most fascinating area within financial economics during the past thirty years. This chapter reviews the evolution of derivatives contract markets and derivatives research over the past thirty years. The chapter has six complementary sections. The first...

This paper provides an empirical study on the predictability of implied volatility using dataset collected from the London over-the-counter currency option market. The present work is motivated by the lack of empirical studies that address implied volatility characteristics across various...

We propose two new risk measures (i-beta and i-gamma) for a stock, which aim to distinguish between noise and information. Noise allows the stock price evolution to happen along a continuous path. Market wide economic information is transmitted via price jumps. Noise is idiosyncratic and does...

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 investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the price of the underlying stock...

We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the...

Using day-end pricing data from a comprehensive data base not readily available outside of China, an algorithm to trade near-the-money call option time spreads on China's SSE 50 ETF was developed and tested. Analysis of in-sample data, suggested profitable trading rules that, when applied to...

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...