Estimating growth at risk with skewed stochastic volatility models

Elias Wolf

No 2022/2, Discussion Papers from Free University Berlin, School of Business & Economics

Abstract: This paper proposes a Skewed Stochastic Volatility (SSV) model to model time varying, asymmetric forecast distributions to estimate Growth at Risk as introduced in Adrian, Boyarchenko, and Giannone's (2019) seminal paper "Vulnerable Growth". In contrary to their semi-parametric approach, the SSV model enables researchers to capture the evolution of the densities parametrically to conduct statistical tests and compare different models. The SSV-model forms a non-linear, non-gaussian state space model that can be estimated using Particle Filtering and MCMC algorithms. To remedy drawbacks of standard Bootstrap Particle Filters, I modify the Tempered Particle Filter of Herbst and Schorfheide's (2019) to account for stochastic volatility and asymmetric measurement densities. Estimating the model based on US data yields conditional forecast densities that closely resemble the findings by Adrian et al. (2019). Exploiting the advantages of the proposed model, I find that the estimated parameter values for the effect of financial conditions on the variance and skewness of the conditional distributions are statistically significant and in line with the intuition of the results found in the existing literature.

Keywords: Growth at Risk; Macro Finance; Bayesian Econometrics; Particle Filters (search for similar items in EconPapers)
JEL-codes: C10 E32 E58 G01 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fdg, nep-mac, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:fubsbe:20222

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