This paper examines the out‐of‐sample forecast performance of sectoral stock market indicators for real GDP, private consumption and investment growth up to 4 quarters ahead in the US and the Euro Area. Our findings are that the predictive content of sectoral stock market indicators: i) is...

We decompose the squared VIX index, derived from US S&P500; options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then...

This paper evaluates how well sectoral stock prices forecast future economic activity compared to traditional predictors such as the term spread, dividend yield, exchange rates and money growth. The study is applied to euro area financial asset prices and real economic growth, covering the...

Using a sample that provides unprecedented detail on foreign listings, new listings, and delistings for 29 exchanges in 24 countries starting from the early 1980s, we document a growing tendency of listings to concentrate in the U.S. and the U.K., and large changes in all exchanges' ability to...

In this paper we compare the in-sample fit and out-of-sample forecasting performance of no-arbitrage quadratic and essentially affine term structure models, as well as the dynamic Nelson-Siegel model. In total eleven model variants are evaluated, comprising five quadratic, four affine and two...

We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the...

Market participants often invest in order to acquire information that pertains to the market itself (e.g. order flow) rather than to fundamentals. This enables them to infer more information from past trades. I show that agents trading on such information, typically high-frequency traders,...

We examine empirically the role of high-frequency traders (HFTs) in price discovery and price efficiency. Based on our methodology, we find overall that HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing...

How do financial markets price new information? This paper analyzes price setting at the intersection of private and public information, by testing whether and how the reaction of financial markets to public signals depends on the relative importance of private information in agents' information...

We study empirically how competition among high-frequency traders (HFTs) affects their trading behavior and market quality. Our analysis exploits a unique dataset, which allows us to compare environments with and without high-frequency competition, and contains an exogenous event - a tick size...