We assume that the guiding principle for banks is to maximize risk-adjusted returns gener-ated by their balance sheets. Instead, banks act rationally in our model they optimally construct a portfolio subject to budget constraints so as to raise cash to satisfy creditors (interbank and external). Unlike in related literature, we relax the assumption that there is an exogenous pecking order of how banks would sell their assets. By doing so, we can embed a well-established fire-sale channel into our model. We propose a model to study contagion effects in a banking system capturing network effects of direct exposures and indirect effects of market behaviour that may impact asset valuation. One reason for this may be related to financial institutions’ incentives and strategic behaviours. Post-crisis reforms addressed many of the causes of this event, but contagion effects may not be fully eliminated. This has recently been seen during the Global Financial Crisis. While it con-tributes to efficiency of financial services, it also creates structural vulnerabilities: pernicious shock transmission and amplification impacting banks’ capitalization. JEL Code C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, CapacityĪbstract Interconnectedness is an inherent feature of the modern financial system. This suggests that there is ample scope for model averaging tools in forecast exercises, notably as they also help to reduce model uncertainty and can be used to assess forecast uncertainty. A pseudo out-of-sample forecast exercise shows that our model averaging approach beats a battery of ambitious benchmark models, including BVARs, FAVARs, LASSO and Ridge regressions. Our results highlight marked cross-country heterogeneity in the key drivers of housing investment which calls for country-specific housing market policies. To account for substantial modelling uncertainty, it estimates many vector error correction models (VECMs) using a wide set of short and long-run determinants and selects the most promising specifications based on in-sample and out-of-sample criteria. Abstract This study applies a model averaging approach to conditionally forecast housing investment in the largest euro area countries and the euro area.
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