What is the market impact of predictable order flow? Leveraged exchange-traded products are useful for answering this question because they generate daily rebalancing flows whose size, sign and timing are predictable. This paper presents new evidence from the market for leveraged volatility...

We forecast monthly Value at Risk (VaR) and Conditional Value at Risk (CVaR) using option market data and four different econometric techniques. Independently from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount...

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...

Divergence in investor beliefs is an important driver of the negative relation between option trading volume and future stock returns. We find a strong negative relation between disagreement-based option trades and future stock returns, and this relation is markedly amplified when the underlying...

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...

Which market has leading informational advantage: stocks or options? Using large set of stock and option characteristics, and machine learning, we provide a comprehensive analysis of which characteristics are the first order importance predictors of options and stock returns. First, we find that...

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...

This paper aims at contributing to the literature in three ways: First, we re-evaluate the performance of popular Value-at-Risk (VaR) estimation methods on freight rates amid the adverse economic consequences of the recent financial and sovereign debt crisis. Secondly we provide a detailed and...

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...

A central consideration for the use of any pricing model is the ability to calibrate that model to market or historical prices. Whether the information needed by the model can be effectively implied from the data or not is one part of the calibration problem. However, in many applications, the...