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BUBBLES AND CRISES – THE ROLE OF CREDIT AND HOUSE PRICES NORGES BANK WP BY ANUNDSEN, GERDRUP, HANSEN, AND KRAGH-SØRENSEN PRESENTED BY KARSTEN GERDRUP AT RBNZ WORKSHOP, 22 OCT 2014 Background Financial crises are costly Banks had too little capital and liquidity prior to the great financial crisis Potentially many new macro-pru tools in the EU regulation following Basel III – Norway is associated to EU through European Economic Agreement (EEA) 2 Motivation for paper Norges Bank responsible for giving advice on the countercyclical capital buffer (CCB) to the Ministry of Finance CCB should be increased when financial imbalances build up Needs an analytical framework for assessing the time dimension of systemic risk – Focus on early warning systems for the build-up phase 3 House prices continue to rise Index. Q1 2005 = 100. Q1 1995 – Q1 2014 180 Belgium Sweden 160 140 Switzerland Norway 120 180 180 160 160 140 140 120 120 Denmark US Netherlands Spain 180 160 140 120 80 100 100 80 80 60 60 60 60 40 40 40 40 20 20 20 20 Source: BIS 100 2013 2011 2009 2007 2005 2003 2001 1999 1997 80 1995 2013 2011 2009 2007 2005 2003 2001 1999 1997 1995 100 4 Main findings in paper Merit in monitoring credit to households and non-financial corporations separately Global housing imbalances have predictive power for financial crises Real-time measures of exuberance in the housing and credit market enter as significant explanatory variables – Probability of a crisis increases markedly when exuberant behavior coincides with high leverage 5 Outline Data Logit model Econometric results Out-of-sample properties and stability Concluding remarks 6 Data Panel of 16 industrialized countries over the period 1970Q1 – 2013Q2 Australia, Belgium, Canada, Finland, France, Germany, Italy, Japan, Korea, Netherlands, Norway, Spain, Sweden, Switzerland, UK and USA 27 identified crises (40 % related to the great financial crisis) Explanatory variables HH credit, NFE credit, and total private credit to GDP Growth in total private credit House prices to disposable income Measures of global credit and house prices (based on country data) Measures of exuberance in credit and house prices (based on country data) Other variables: GDP growth, non-core funding ratio, bank leverage, country-fixed effects Variables enters as growth rates or gaps (one-sided HP filter on data extended with recursive forecasts, λ=400000) 7 Behaviour of variables around crises Is the average development in pre- and post-crisis periods significantly different compared to «normal/tranquil times»? Inspired by Gourinchas and Obstfeld (2012), we consider a the following specification: 𝑦𝑖,𝑡 = 𝛼𝑖 + 𝛽′𝑠 𝛿𝑖,𝑠 + 𝜀𝑖𝑡 𝑦𝑖,𝑡 is the explanatory variable 𝛿𝑖,𝑠 takes the value 1 when country i is s quarters away from the start of a crisis, and 0 otherwise 𝛽𝑠 is the conditional effect on 𝑦𝑖,𝑡 of being s quarters away from the start of a crisis episode relative to «normal times» The event window is set (ad hoc) to 𝑠 = (+/−) 1,….,16 8 Total credit House prices Noncore funding 9 Decomposing credit HH credit NFE credit 10 Outline Data Logit model Econometric results Out-of-sample properties and stability Concluding remarks 11 Logit model Independent variable: 𝑌𝑖,𝑡 = 1 𝑖𝑓𝐹𝐶𝑖,𝑡+𝑘 = 1 for k ∈ [5,12] 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 The probability of being in a pre-crisis state is given by: 𝑃𝑟𝑜𝑏 𝑌𝑖,𝑡 = 1 = exp α𝑖 + 𝛽′𝒙𝑖,𝑡 1 + exp α𝑖 + 𝛽′𝒙𝑖,𝑡 , where 𝒙𝑖 is a vector of explanatory variables and α𝑖 country fixed effects In some specifications we include an indicator variable for exuberance: 𝐼 𝐸𝑥𝑢𝑏𝑒𝑟𝑎𝑛𝑐𝑒 = 1 𝑖𝑓 𝐸𝑥𝑢𝑏𝑒𝑟𝑎𝑛𝑐𝑒(𝑥𝑖,𝑡 ) ≥ 0 0 𝑖𝑓 𝐸𝑥𝑢𝑏𝑒𝑟𝑎𝑛𝑐𝑒 𝑥𝑖,𝑡 < 0 Measures for global credit-to-GDP and house prices-to-income are based on trade-weights 12 More on the exuberance indicators Present value of an asset (house price) should be equal to the expected discounted stream of pay-offs (imputed rent) The price of a house at time t can be written as: ∞ 𝑃𝐻𝑡 = 𝐸𝑡 𝑖=1 1 1+𝑟 𝑖 𝑅𝑡+1 + 𝐵𝑡 In the absence of explosive behaviour, house prices (𝑃𝐻𝑡 ) and rents (𝑅𝑡+1 ) follows the same stochastic process (are cointegrated) and the «bubble» component 𝐵𝑡 = 0 We follow a framework suggested by e.g. Pavalidis et al. (2014) to test (recursively) whether 𝐵𝑡 > 0 13 Exuberance in selected countries 14 Outline Data Logit model Econometric results Out-of-sample properties and stability Concluding remarks 15 Regression results – global variables 16 Regression results – measures of exuberance 17 Driving forces behind vulnerabilities Decomposition of change in crisis probabilities 18 Outline Data Logit model Econometric results Out-of-sample properties and stability Concluding remarks 19 Out-of-sample properties 1. Use data up to 2000 to estimate the parameters of our models and then make forecasts for the period 2000-2012 Able to forecast (reasonably) the great financial crisis Reasonable AUROC that is comparable to the credit-to-GDP gap 2. Estimate the probability of a crisis for each country (over the whole sample period) using data for all other countries A lot of information in a country’s own history, but still reasonable AUROC that is comparable to the credit-to-GDP gap 20 Temporal stability 21 Global house prices – now and then Behaviour of global house prices-to-income gap around crises Great financial crisis Earlier episodes 22 Concluding remarks We confirm that policy-makers should pay attention to early warning indicators such as credit-to-GDP gap, non-core funding gap, and house price-to-income gap Merit in monitoring credit to households and non-financial companies separately Found a role for global house prices in predicting financial crises, in particular the great financial crisis (policy implications?) Econometric measures of exuberance were tested. The effect of domestic housing market exuberance particularly important. The road ahead: Crisis probability as a measure of financial imbalances to be used both in monetary policy considerations and macro-pru 23 BUBBLES AND CRISES – THE ROLE OF CREDIT AND HOUSE PRICES NORGES BANK WP BY ANUNDSEN, GERDRUP, HANSEN, AND KRAGH-SØRENSEN PRESENTED BY KARSTEN GERDRUP AT RBNZ WORKSHOP, 22 OCT 2014