Measuring aggregate risk: Can we robustly identify asset-price boom–bust cycles?

Borgy, Vladimir; Clerc, Laurent; Renne, Jean‐Paul

doi:10.1016/j.jbankfin.2014.05.015

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…The house price index has been chosen by the Commission as one of the headline indicators, since boom and bust in housing markets can be a source of macroeconomic imbalances. Later, Borgy, Clerc and Renne (2014) found evidence that house price booms are more likely to turn into a costly recession, or to trigger a banking crisis, than stock price booms. This finding is consistent with Barrell et al (2010a), who showed that house price booms were a good leading indicator of banking crises, along with other indicators, in a logit early warning system framework.…”

Section: The Macroeconomic Imbalance Proceduresmentioning

confidence: 99%

Analysing the Relevance of the MIP Scoreboard's Indicators

Domonkos¹,

Ostrihoň

Sikulova

et al. 2017

Natl. Inst. econ. rev.

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The EU established an early warning system by introducing the Macroeconomic Imbalance Procedure (MIP) in the wake of the recent recession. Nevertheless, it has been found by some authors to be rather vague when launching the Excessive Imbalances Procedure. Performed analysis reflects on such views and treats the MIP indicators as a system while assessing the significance of all particular variables separately. This assessment was accomplished by applying a multivariate unbalanced logit model, utilising all 14 MIP headline indicators, using time horizons ranging from one to three years before crisis, which was represented by periods with output gap lower than negative 2 per cent. The approach was confronted with the estimates of a linear probability model to provide an idea about the robustness of the results. In the short term, activity rates, youth unemployment rates and private sector debt are the best performing indicators, complemented by current account balances in the long term.

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Section: The Macroeconomic Imbalance Proceduresmentioning

confidence: 99%

Analysing the Relevance of the MIP Scoreboard's Indicators

Domonkos¹,

Ostrihoň

Sikulova

et al. 2017

Natl. Inst. econ. rev.

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“…22 This yields a sample size of approximately 8000 state-month observations, which we split into two datasets to accommodate both in-and out-of-sample testing. The first dataset is truncated at 2001 so we can perform out-of-sample back tests using data from the recent credit 22 Alternatively, we could have chosen downturns based on the distance from the long-term trend (instead of the number of years of negative house price appreciation) as done by Borgy et al (2014). We avoid this specification because it would bind the CLB from above and could severely bias our estimation of the CLB depth depending on the percentage threshold used.…”

Section: Theoretical Basis and Empirical Approachmentioning

confidence: 99%

How Low Can House Prices Go? Estimating a Conservative Lower Bound

Bogin

Bruestle

Doerner

2015

J Real Estate Finan Econ

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In risk management, the credit risk and required capital associated with mortgage assets is often estimated through stress testing where the house price path is an important determinant of the severity of the stress test. Specifically, the extent of credit-related losses is a function of how far house prices are above long-term trend and the extent to which they can fall below trend. Focusing on the latter, we develop a theoretically-based statistical technique to identify a conservative lower bound (CLB) for house prices. Leveraging a model based upon investor incentives, the CLB explains the depth of housing market downturns at both the national and state level over a variety of market environments. This approach performs well in several historical back tests and has strong out-of-sample predictive ability. When back-tested, the estimation approach does not understate house price declines in any state over the 1987 to 2001 housing cycle and only understates declines in three states during the most recent financial crisis. This latter result is particularly noteworthy given that the post-2001 estimates are performed out-of-sample. The CLB is attractive because it (1) provides a leading indicator of the severity of future downturns and (2) allows estimates of trough to recover or decrease in magnitude as markets return to baseline conditions. This estimation technique could prove helpful in measuring the credit risk associated with portfolios of mortgage assets as part of evaluating static or designing dynamic stress tests.

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“…In a previous paper with Borgy et al (2014), I explored the ability of a system of early indicators to detect ex-ante the build-up of financial imbalances, in particular in housing markets, for a similar set of OECD countries. The three most robust and powerful early warning indicators to detect costly 2 real house price booms are real interest rates (both short and long) and real stock prices.…”

Section: Synchronicity: the Role Of Global Factorsmentioning

confidence: 99%

Hot Property

Nijskens¹,

Lohuis²,

Hilbers³

et al. 2019

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Measuring aggregate risk: Can we robustly identify asset-price boom–bust cycles?

Cited by 16 publications

References 39 publications

Analysing the Relevance of the MIP Scoreboard's Indicators

Analysing the Relevance of the MIP Scoreboard's Indicators

How Low Can House Prices Go? Estimating a Conservative Lower Bound

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