Forecasting Financial Vulnerability in the US: A Factor Model Approach

Hyeongwoo Kim (gmmkim@gmail.com) and Wen Shi

MPRA Paper from University Library of Munich, Germany

Abstract: This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.

Keywords: Financial Stress Index; Method of the Principal Component; Out-of-Sample Forecast; Ratio of Root Mean Square Prediction Error; Diebold-Mariano-West Statistic; Ordered Probit Model (search for similar items in EconPapers)
JEL-codes: E44 E47 G01 G17 (search for similar items in EconPapers)
Date: 2018-10
New Economics Papers: this item is included in nep-for, nep-mac and nep-rmg
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https://mpra.ub.uni-muenchen.de/89766/1/MPRA_paper_89766.pdf original version (application/pdf)

Related works:
Journal Article: Forecasting financial vulnerability in the USA: A factor model approach (2021)
Working Paper: Forecasting Financial Vulnerability in the US: A Factor Model Approach (2020)
Working Paper: Forecasting Financial Vulnerability in the US: A Factor Model Approach (2018)
Working Paper: Forecasting Financial Vulnerability in the US: A Factor Model Approach (2016)
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