Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions


Book Description

A rapidly growing body of research has examined tail risks in macroeconomic outcomes, commonly using quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. This pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in macroeconomic forecast distributions and outcomes. Even though the 1-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.




A New Heuristic Measure of Fragility and Tail Risks


Book Description

This paper presents a simple heuristic measure of tail risk, which is applied to individual bank stress tests and to public debt. Stress testing can be seen as a first order test of the level of potential negative outcomes in response to tail shocks. However, the results of stress testing can be misleading in the presence of model error and the uncertainty attending parameters and their estimation. The heuristic can be seen as a second order stress test to detect nonlinearities in the tails that can lead to fragility, i.e., provide additional information on the robustness of stress tests. It also shows how the measure can be used to assess the robustness of public debt forecasts, an important issue in many countries. The heuristic measure outlined here can be used in a variety of situations to ascertain an ordinal ranking of fragility to tail risks.




Macroeconomic Tail Risks and Asset Prices


Book Description

I document that dividend growth and returns on the aggregate U.S. stock market are more correlated with consumption growth in bad economic times. In a consumption-based asset pricing model with a generalized disappointment averse investor and small, IID consumption shocks, this feature results in a realistic equity premium despite low risk aversion. The model is consistent with the main facts about stock market risk premia inferred from equity index options, remains tightly parameterized, and allows for analytical solutions for asset prices. An extension with non-IID dynamics accounts for excess volatility and return predictability while preserving the model's consistency with option moments.




Microeconomic Origins of Macroeconomic Tail Risks


Book Description

We document that even though the normal distribution is a good approximation to the nature of aggregate fluctuations, it severely under-predicts the frequency of large economic downturns. We then provide a model that can explain these facts simultaneously. Our model show that the propagation of microeconomic shocks through input-output linkages can fundamentally reshape the distribution of aggregate output, increasing the likelihood of large downturns (macroeconomic tail risks) from infinitesimal to substantial. For example, an economy subject to thin-tailed micro shocks but with "unbalanced" input-output linkages (where some sectors or firms play a much more important role than others as inputs suppliers to the rest of the economy) may exhibit deep recessions as frequently as economies that are subject to heavy-tailed shocks. This is despite the fact that a central limit theorem-type result would imply that aggregate output is normally distributed. We characterize what types of input-output linkages and distributions of microeconomic shocks lead to sizable macroeconomic tail risks, and also show how the same economic forces cause the output of many sectors to simultaneously fall by large amounts. Keywords: Business cycles, macroeconomic tail risks, input-output linkages. JEL Classification: C67, E32.




Financial Conditions, Macroeconomic Uncertainty, and Macroeconomic Tail Risks


Book Description

This paper investigates how financial conditions and macroeconomic uncertainty jointly affect macroeconomic tail risks. We first document that tight financial conditions decrease all conditional quantiles of future output growth in the near term, while high macroeconomic uncertainty stretches the interquartile range, leaving the median intact. Because financial conditions and uncertainty comove substantially, the conditional means and variances shift simultaneously in the opposite direction. Consequently, the downside risk varies much more than the upside risk. Using a structural VAR, we find that both financial and uncertainty shocks tighten financial conditions and heighten macroeconomic uncertainty instantaneously. Therefore, all conditional quantiles of output growth decrease disproportionately in response to both shocks, and the conditional distribution of output growth not only shifts but also skews to the left, leading to greater growth vulnerability for 2 to 3 years in the future.




Forecasting Macroeconomic Tail Risk in Real Time


Book Description

We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions (now- and forecasts) of employment, output, inflation and consumer sentiment. Our results suggest that news data contain valuable information not captured by economic indicators, particularly for left-tail forecasts. Methods that capture quantile-specific non-linearities produce superior forecasts relative to methods that feature linear predictive relationships. However, adding news-based data substantially increases the performance of quantile-specific linear models, especially in the left tail. Variable importance analyses reveal that left tail predictions are determined by both economic and textual indicators, with the latter having the most pronounced impact on consumer sentiment.




Skewed SVARs


Book Description