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Julia Schaumburg
- 13 February 2023
- WORKING PAPER SERIES - No. 2780Details
- Abstract
- We introduce a new dynamic clustering method for multivariate panel data char-acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations towards the current center of their previous cluster as-signment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020.
- JEL Code
- C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
G22 : Financial Economics→Financial Institutions and Services→Insurance, Insurance Companies, Actuarial Studies
- 29 July 2021
- WORKING PAPER SERIES - No. 2577Details
- Abstract
- We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM). Finally, the HMM’s transition matrix can depend on lagged time-varying cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of challenging settings. Incorporating dynamics in the cluster composition proves empirically important in an a study of 299 European banks between 2008Q1 and 2018Q2. We find that approximately 3% of banks transition per quarter on average. Transition probabilities are in part explained by differences in bank profitability, suggesting that low interest rates can lead to long-lasting changes in financial industry structure.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
- 18 March 2021
- WORKING PAPER SERIES - No. 2532Details
- Abstract
- We introduce a flexible, time-varying network model to trace the propagation of interest rate surprises across different maturities. First, we develop a novel econometric framework that allows for unknown, potentially asymmetric contemporaneous spillovers across panel units, and establish the finite sample properties of the model via simulations. Second, we employ this innovative framework to jointly model the dynamics of interest rate surprises and to assess how various monetary policy actions, for example, short-term, long-term interest rate targeting and forward guidance, propagate across the yield curve. We find that the network of interest rate surprises is indeed asymmetric, and defined by spillovers between adjacent maturities. Spillover intensity is high, on average, but shows strong time variation. Forward guidance is an important driver of the spillover intensity. Pass-through from short-term interest rate surprises to longer maturities is muted, yet there are stronger spillovers associated with surprises at medium- and long-term maturities. We illustrate how our proposed framework helps our understanding of the ways various dimensions of monetary policy propagate through the yield curve and interact with each other.
- JEL Code
- C21 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy - Network
- ECB Lamfalussy Fellowship Programme
- 8 September 2017
- WORKING PAPER SERIES - No. 2098Details
- Abstract
- We study the impact of increasingly negative central bank policy rates on banks’ propensity to become undercapitalized in a financial crisis (‘SRisk’). We find that the risk impact of negative rates is moderate, and depends on banks’ business models: Banks with diversified income streams are perceived by the market as less risky, while banks that rely predominantly on deposit funding are perceived as more risky. Policy rate cuts below zero trigger different SRisk responses than an earlier cut to zero.
- JEL Code
- G20 : Financial Economics→Financial Institutions and Services→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages - Network
- Research Task Force (RTF)
- 29 June 2017
- WORKING PAPER SERIES - No. 2084Details
- Abstract
- 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.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models - Network
- Research Task Force (RTF)