This paper reconsiders the effect of investor sentiment on stock prices. Using survey-based sentiment indicators from Germany and the US we confirm previous findings of predictability at intermediate time horizons. The main contribution of our paper is that we also analyze the immediate price...

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine...

The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market...

Futures markets are a potentially valuable source of information about market expectations. Exploiting this information has proved difficult in practice, because the presence of a timevarying risk premium often renders the futures price a poor measure of the market expectation of the price of...

Low probability events are overweighted in the pricing of out-of-the-money index puts and single stock calls. We find that this behavioral bias is strongly time-varying, linked to equity market sentiment, and higher moments of the risk-neutral density. An implied volatility (IV) sentiment...

This paper investigates how biases in macroeconomic forecasts are associated with economic surprises and market responses across asset classes around US data announcements. We find that the skewness of the distribution of economic forecasts is a strong predictor of economic surprises, suggesting...

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...