This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...

We study the effects of stock market volatility on risk-taking and financial crises by constructing a cross-country database spanning up to 211 years and 60 countries. Prolonged periods of low volatility have strong in-sample and out-of-sample predictive power over the incidence of banking...

We inspect the price volatility before, during, and after financial asset bubbles in order to uncover possible commonalities and check empirically whether volatility might be used as an indicator or an early warning signal of an unsustainable price increase and the associated crash. Some...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

We show that in a general equilibrium model with heterogeneity in risk aversion or belief, shifting wealth from an agent who holds comparatively fewer stocks to one who holds more reduces the equity premium. Since empirically the rich hold more stocks than do the poor, inequality should predict...

This paper investigates the empirical relevance of inflation expectations in forecasting exchange rates. To that end, we use an expectation version of purchasing power parity (EVRPPP) based on the differential of inflation expectations derived from inflation-indexed bonds for Brazil, Colombia,...

We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like 'fear', 'risk', 'hedging', 'opinion', and, 'crisis', as well as 'positive' and 'negative' sentiments, in news articles from the Financial Times. We assess...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

This research utilises a non-linear Smooth Transition Regression (STR) approach to modelling and forecasting the exchange rate, based on the Taylor rule model of exchange rate determination. The separate literatures on exchange rate models and the Taylor rule have already shown that the...

We introduce a portfolio friction in a two-country DSGE model where investors face a constant probability to make new portfolio decisions. The friction leads to a more gradual portfolio adjustment to shocks and a weaker portfolio response to changes in expected excess returns. We apply the model...