Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Dividend suspensions and cash flows during the Covid-19 pandemic: A dynamic econometric model.

Firms suspended dividend payments in unprecedented numbers in response to the outbreak of the Covid-19 pandemic. We develop a multivariate dynamic econometric model that allows dividend suspensions to affect the conditional mean, volatility, and jump probability of growth in daily industry-level dividends and demonstrate how the parameters of this model can be estimated using Bayesian Gibbs sampling methods. We find considerable heterogeneity across industries in the dynamics of daily dividend growth and the impact of dividend suspensions.

Bull and bear markets during the COVID-19 pandemic.

The COVID-19 pandemic has caused severe disruption worldwide. We analyze the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate our model's performance in capturing periods of significant regime change, and provide weekly forecasts that improve risk management and investment decisions. An investment strategy that uses out-of-sample forecasts for market states outperforms a buy and hold strategy during the pandemic by a wide margin, both in terms of annualized returns and Sharpe ratios.