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A time-varying skewness model for Growth-at-Risk

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  • Martin Iseringhausen

    (ESM)

Abstract

This paper studies macroeconomic risks in a panel of advanced economies based on a stochastic volatility model in which macro-financial conditions shape the predictive growth distribution. We find sizable time variation in the skewness of these distributions, conditional on the macro-financial environment. Tightening financial conditions signal increasing downside risk in the short term, but this link reverses at longer horizons. When forecasting downside risk, the proposed model, on average, outperforms existing approaches based on quantile regression and a GARCH model, especially at short horizons. In forecasting upside risk, it improves the average accuracy across all horizons up to four quarters ahead. The suggested approach can inform policy makers' assessment of macro-financial vulnerabilities by providing a timely signal of shifting risks and a quantification of their magnitude.

Suggested Citation

  • Martin Iseringhausen, 2021. "A time-varying skewness model for Growth-at-Risk," Working Papers 49, European Stability Mechanism.
  • Handle: RePEc:stm:wpaper:49
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    References listed on IDEAS

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    Cited by:

    1. Wolf, Elias, 2022. "Estimating growth at risk with skewed stochastic volatility models," Discussion Papers 2022/2, Free University Berlin, School of Business & Economics.
    2. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.

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    More about this item

    Keywords

    Bayesian analysis; downside risk; macro-financial linkages; time variation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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