We show theoretically that lower tail dependence (χ), a measure of the probability that a portfolio will suffer large losses given that the market does, contains important information for risk-averse investors. We then estimate χ for a sample of DJIA stocks and show that it differs...

An analysis of six stock market calendar and weather anomalies from 1980 to 2003 shows that (1) returns on trading days in which macroeconomic announcements were made generate the anomalies and (2) five of the six anomalies are not present at all on the trading days in which such announcements...

For years, researchers have been puzzled by why so few people purchase fixed, immediate, lifetime annuities for their retirement portfolios. Rational theories have been proposed, but none can fully explain the small size of the actual market. Very recently, academics have turned their attention...

This paper presents a test of the response of stock prices to Federal Reserve policy shocks using a Markov-switching framework. The framework endogenously identifies two distinct regimes. The first is a state where the S&P 500 index exhibits a significantly negative response to unexpected...

In the first experimental test of the January effect, we find an economically large and statistically significant result in two very different auction environments. After controlling for variables that could influence subjectsÕ bids such as differences in private values, cumulative earnings,...

The purpose of this paper is to (1) develop a model to show how imperfect information can create excess volatility in asset returns and (2) provide empirical evidence consistent with the model. In this framework, variations in information quality cause the market prices to fluctuate more than...

Using high-frequency data in a Markov-switching framework, we identify states that imply different responses of the yield curve to unexpected changes in the federal funds target. Empirical estimates reveal a low-volatility state where long-term bonds respond significantly, and in a predictable...