How to Predict Financial Stress? An Assessment of Markov Switching Models

Abstract

This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is owing to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the Markov switching model is outperforming the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress.

Seismonomics: Listening to the Heartbeat of the Economy

“…In August 2009, the upturn is identified for the nowcasting and backcasting area while for the COVID‐19 crisis the model can only detect the downturn in the backcasting horizons. The difficulty in the prediction of a downturn induced by such an exogenous abrupt and deep drop in the industrial VA growth would suggest a possible extension of this work through the use of machine learning techniques such as those used by Bluwstein et al (2020) or more formal turning point detection methods such as Markov switching techniques (Duprey & Klaus, 2017). This would allow an investigation of the non‐linear effects of seismic data on the dependent variable and will be the object of a future study.…”

Untitled

“…The research has endeavoured to combine the two approaches in order to try to evaluate the potential predictability of risk materialization measured via the financial stress indicator, based on the indicators that are preferred in EWS (early warning system) models. The first paper to attempt this was that of Duprey and Klaus (2017), in which a potential is seen in combining the best of both approaches. Thus, this research applies the regime-switching methodology of modelling the financial stress indicator for Croatia, to evaluate predictive possibilities of a number of indicators usually used in the EWS approach to the build-up of financial system vulnerabilities.…”

“…However, as Christensen and Li (2014) point out, the decision-making process should rely on point forecasts and insights into the likelihood of the occurrence of the stress event. That is why this study focuses on estimating the effects of the dynamics of indicators on the future probability of entering a stress event, which is rarely found in the literature; to the knowledge of the author, the only other existing study framed in such terms is Duprey and Klaus (2017;. Furthermore, an extensive analysis is made of over several hundred variants of indicators of cyclical risk accumulation.…”

Forecasting financial vulnerability in the USA: A factor model approach

“…Duprey and Klaus (2017) predicted phases of the financial stress periods for a sample of 15 EU in a Markov switching (MS) framework. They found the debt service ratio and property market variables helpful in predicting high financial stress periods.…”