We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as...

This paper evaluates in-sample and out-of-sample stock return predictability with inflation and output gap, the variables that typically enter the Federal Reserve Bank's interest rate setting rule. To examine the role of monetary policy fundamentals for stock return predictability, we introduce...

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time...

We empirically evaluate the predictive power of money growth measured by M2 for stock returns of the S&P 500 index. We use monthly US data and predict multiperiod returns over 1, 3, and 5 years with long-horizon regressions. In-sample regressions show that money growth is useful for predicting...

Economic assets can be classified into two broad categories: those earning an inherent return and those earning a fiat money return. This article shows that both are valued according to the same general principle based on GDP (a constant equal to expected long term real per capita GDP growth)...

This paper calibrates a class of jump-diffusion long-run risks (LRR) models to quantify how well they can jointly explain the equity risk premium and the variance risk premium in the U.S. financial markets, and whether they can generate realistic dynamics of riskneutral and realized...

We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...

In this paper we consider two cases of pairs trading strategies: a conditional statistical arbitrage method and an implicit statistical arbitrage method. We use a simulation-based Bayesian procedure for predicting stable ratios, defined in a cointegration model, of pairs of stock prices. We show...

This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and...