Multimodality in Macro-Financial Dynamics

Adrian, Tobias; Boyarchenko, Nina; Giannone, Domenico

doi:10.2139/ssrn.3489435

Cited by 13 publications

(26 citation statements)

References 51 publications

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“…Focusing on US data, they have found that the lower quantiles of GDP growth vary with financial conditions while the upper quantiles are stable over time, therefore pointing to an asymmetric and nonlinear relationship between financial and real variables. Adrian et al (2018) have confirmed this result extending the sample to different countries and have pushed this view further by defining the concept of growth at risk as GDP growth at the lower fifth percentile of the GDP growth distribution, conditional on an index of financial stress. Building on this work, several recent papers have explored the idea, while policy institutions have adopted the methodology to monitor risk in different countries (see, for example, Prasad et al, 2019 for a description of the use of this method at the IMF).…”

Section: Introductionsupporting

confidence: 55%

“…The appeal of this approach to policy work is that it provides a framework in which forecasting can be thought of as a risk managing exercise (see Kilian and Manganelli, 2008 for the first development of this idea). This is the core idea of what is referred to as the Growth at Risk (GaR) framework of Adrian et al (2018). 1 A separate line of research pioneered by the BIS has stressed the importance of the leverage cycle as an indicator of risk, while several studies have pointed at a correlation of excess growth in leverage and financial crisis (see Jorda et al, 2011, Schularick andTaylor, 2012, Jorda et al, 2013 and related literature) and found that recessions preceded by financial crises are deeper and followed by slower recoveries (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Financial Variables as Predictors of Real Growth Vulnerability

Reichlin

Ricco

Hasenzagl³

2020

SSRN Journal

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We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks-the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology.

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Section: Introductionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Financial Variables as Predictors of Real Growth Vulnerability

Reichlin

Ricco

Hasenzagl³

2020

SSRN Journal

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“…In this example, the 95 percent quantile of the predictive distribution changes relatively little, with values of 1.65 in period 1 and 1.29 in period 2. The 5 percent quantile drops significantly, from -1.65 in period 1 to -5.29 in 4 Jensen, et al (2020) also develop a dynamic stochastic general equilibrium model consistent with their empirical findings. Adrian, et al (2020) extend an econometric formulation of a DSGE model to include endogenous risk.…”

Section: Introductionmentioning

confidence: 70%

“…time, which they link to the increased financial leverage of households and firms. 4 At a practical level, monetary policymakers have commonly treated forecast distributions as being potentially asymmetric, at least at some points in time. The Bank of England's well-known fan charts for inflation are constructed with a two-piece normal distribution to reflect asymmetries as judged by the Monetary Policy Committee.…”

Section: Introductionmentioning

confidence: 99%

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Carriero

Clark

Marcellino

2020

Working Paper (Federal Reserve Bank of Cleveland)

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A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on 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. We note that 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). Building on that insight, we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification with a common factor in volatility that enters the VAR's conditional mean. Even though the one-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, the model estimates yield more time variation in downside risk as compared to upside risk. Results from the model that includes a volatility factor in the conditional mean and thereby allows for asymmetries in conditional distributions are very similar. Our paper also extends the recent literature by formally evaluating the accuracy of tail risk forecasts and assessing the performance of Bayesian quantile regression, as well as our Bayesian VARs, in this context. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.

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“…1 Earlier work of Manzan (2015) used quantile regression to assess the value of a large number of macroeconomic indicators in forecasting the complete distribution of some key variables. 2 The interest in tail risks reflects an underlying perception or assumption of asymmetries in distributions of outcomes. Some form of asymmetry has long been incorporated in particular economic models (e.g., Markov switching or threshold models; in a recent example, Alessandri and Mumtaz (2017) use threshold models to assess output forecasts in periods of financial distress).…”

Section: Introductionmentioning

confidence: 99%

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Carriero

Clark

Marcellino

2020

Working Paper (Federal Reserve Bank of Cleveland)

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A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks. In this paper we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification featuring a common volatility factor that is a function of past financial conditions. Even though the conditional predictive distributions from the VAR models are symmetric, our estimated models featuring time-varying volatility yield more time variation in downside risk as compared to upside risk-a feature highlighted in other work that has advocated for quantile regression methods or focused on asymmetric conditional distributions. Overall, the BVAR models perform comparably to quantile regression for estimating tail risks, with, in addition, some gains in standard point and density forecasts.

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Multimodality in Macro-Financial Dynamics

Cited by 13 publications

References 51 publications

Financial Variables as Predictors of Real Growth Vulnerability

Financial Variables as Predictors of Real Growth Vulnerability

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

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