Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

We propose a novel observation-driven finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Student's t distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classified reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1-2015Q4, we identify six business model components and discuss how their properties evolve over time. Changes in the yield curve predict changes in average business model characteristics.

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“…For simplicity and parsimony, we consider the integrated score-driven dynamics as discussed by Lucas and Zhang (2016),…”

Section: Normal Mixture With Time-varying Meansmentioning

confidence: 99%

Bank Business Models at Zero Interest Rates

Lucas

Schaumburg

Schwaab

2018

Journal of Business & Economic Statistics

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“…The component means are updated using the score dynamics of Creal, Koopman, and Lucas (2013); see also Harvey (2013) and Creal et al (2014). For simplicity and parsimony, we consider the integrated score-driven dynamics as discussed in Lucas and Zhang (2015),…”

Section: Dynamic Normal Mixture Model With Time-varying Meansmentioning

confidence: 99%

Bank Business Models at Zero Interest Rates

2016

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“…Time-variation in location and scale parameters is modeled following the score-driven approach as introduced by Creal et al (2013) and Harvey (2013). We impose further parsimony by using the exponentially weighted score-driven dynamics of Lucas and Zhang (2016). For the time-varying means, we specify…”

Section: Time-varying Meansmentioning

confidence: 99%

“…Following the exponentially weighted score-driven dynamics of Lucas and Zhang (2016), the transition equation for the time-varying covariance matrices Σ jt is given by…”

Section: Time-varying Covariance Matricesmentioning

confidence: 99%

Dynamic Clustering of Multivariate Panel Data

et al. 2019

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We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of interest can transition between clusters over time based on a Hidden Markov model (HMM). Finally, the HMM's transition matrix can depend on lagged cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of settings. An empirical study of 299 European banks between 2008Q1 and 2018Q2 suggests that banks have become less diverse over time in key characteristics. On average, approximately 3% of banks transition each quarter. Transitions across clusters are related to cluster dissimilarity and differences in bank profitability.

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Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Cited by 9 publications

References 30 publications

Bank Business Models at Zero Interest Rates

Bank Business Models at Zero Interest Rates

Bank Business Models at Zero Interest Rates

Dynamic Clustering of Multivariate Panel Data

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