We find that lower ex-ante earnings volatility leads to higher Post-Earnings Announcement Drift (PEAD). PEAD is a function of both the magnitude of an earnings surprise and its persistence. While prior research has largely investigated market reactions to the magnitude of the earnings surprise,...

We use learning in an equilibrium model to explain the puzzling predictive power of the volatility risk premium (VRP) for option returns. In the model, a representative agent follows a rational Bayesian learning process in an economy under incomplete information with the objective of pricing...

We derive a formula for the expected return on a stock in terms of the risk-neutral variance of the market and the stock's excess risk-neutral variance relative to the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The...

We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the 'number of jumps' as a variable to explain and predict volatility and show that the number of jumps in...

This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of macroeconomic and financial variables. Complete subset...

This paper addresses this question with an asset-pricing model featuring endogenous corporate policies. Long-run risk reflects a firm's profit exposure to slowly-moving expected consumption growth, whereas short-run risk captures the exposure to frequent unexpected changes in consumption growth....

An accurate forecast of intraday volume is a key aspect of algorithmic trading. This manuscript proposes a state-space model to forecast intraday trading volume via the Kalman filter and derives closed-form expectation-maximization (EM) solutions for model calibration. The model is extended to...

The application of artificial neural networks to finance has received a great deal of attention from both investors and researchers, especially as a forecasting method. When the number of predictors is high, these methods suffer from the so-called "curse of dimensionality" and produce biased...

This paper examines whether financial statement information can predict future realized volatility incremental to the volatility implied by option market prices. Prior research establishes that option-implied volatility is a biased estimator of future realized volatility. I use an analytical...

Forecasting Realized Volatility (RV) is of paramount importance for both academics andpractitioners. During recent decades, academic literature has made substantial progressboth in terms of methods and predictors under consideration. Despite the popularity oftechnical indicators, there has been...