Measuring Financial Cycles with a Model-Based Filter: Empirical Evidence for the United States and the Euro Area

Galati, Gabriele; Hindrayanto, Irma; Koopman, Siem Jan; Vlekke, Marente

doi:10.2139/ssrn.2722547

Cited by 27 publications

(43 citation statements)

References 37 publications

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“…In line with previous studies, we deflate the latter two series with the GDP deflator. Note further that, since our model is invariant to linear transformations, our estimates also imply a trend‐cycle decomposition for the credit‐to‐GDP ratio, which has been considered by various related studies (e.g., Drehmann et al, ; Galati et al, ; Giese et al, ).…”

Section: Resultsmentioning

confidence: 83%

Business, housing, and credit cycles

Rünstler

Vlekke

2017

J of Applied Econometrics

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120

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. Terms of use: Documents in AbstractWe use multivariate unobserved components models to estimate trend and cyclical components in GDP, credit volumes and house prices for the U.S. and the five largest European economies. With the exception of Germany, we find large and long cycles in credit and house prices, which are highly correlated with a medium-term component in GDP cycles. Differences across countries in the length and size of cycles appear to be related to the properties of national housing markets. The precision of pseudo real-time estimates of credit and house price cycles is roughly comparable to that of GDP cycles. Studies have so far mostly used univariate non-parametric methods, i.e. band-pass filters and turning point analysis, to extract financial cycles. These methods require a priori assumptions on cyclical characteristics and are applied separately to each series. They are therefore not particularly suitable for assessing cyclical co-movements and evaluating the cyclical stance in real-time.Multivariate STSMs do not suffer from these shortcomings. We propose various modifications of the standard STSM to jointly model the cyclical dynamics in GDP and the financial series.In line with earlier studies, we find pronounced cycles in credit and house prices with a length of 12 to 18 years in most cases. There also arise important differences across countries. We find cycles to be particularly long and large for the U.K. and Spain, of more moderate length and size for the U.S., Italy, and France, and comparatively short and small for Germany. These differences turn out to be related to a specific structural characteristic of national housing markets: cycles are longer and larger for countries with high rates of private home ownership.Moreover, financial cycles are closely related to a medium-term component in the GDP cycle.More precisely, the multivariate estimates emphasize the presence of medium-term fluctuations in GDP, which are longer than the traditional business cycle (3 to 8 years). We find high correlations between the financial and GDP cycles in the medium term, but more moderate correlations over the business cycle. While previous studies have documented that peaks and troughs in financial cycles coincide with major turning points in ...

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Section: Resultsmentioning

confidence: 83%

Business, housing, and credit cycles

Rünstler

Vlekke

2017

J of Applied Econometrics

Self Cite

120

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“…International research for the Kalman filter include numerous studies analysing business cycles, that is, Valle Azevedo, Koopman and Rua (2006), Koopman and Azevedo (2008) and Creal, Koopman and Zivot (2010); however, few have expressly focussed on financial variables. Galati et al (2016) attempted to measure financial cycles for the United States and the Euro using a Kalman filter and found that financial cycles are longer than business cycles and have higher amplitudes. Extracting cycles from business failure rates, real GDP and credit spreads, Koopman and Lucas (2005) found comparable mediumterm cycles for U.S. data.…”

Section: A Classical Methodology Focuses Purely On Changes In the Lmentioning

confidence: 99%

“…Bottom-up data also produced substantial individual components, suggesting that differences in the values of bank-adjusted factors would be large even under periods of broad financial stability (Drehmann et al 2010). Galati et al (2016) assert that previous investigations into financial cycles and their statistical properties focussed on three approaches. Firstly, attempts to identify turning points focussed on positions of peaks and troughs.…”

Section: Countercyclical Capital Buffermentioning

confidence: 99%

“…Secondly, Galati et al (2016) suggest that statistical frequency-based filters include the HP filter, the Baxter-King filter (1999) and the Christiano and Fitzgerald (2003) filter. These require prespecification of the range of cycle frequencies and thus are referred to as non-parametric filters.…”

Section: Measurement Techniquesmentioning

confidence: 99%

“…Their research finds a multivariate HP filter to be most effective as its endpoint uncertainty is the least, with the result that new data do not materially affect previous estimates too much. Galati et al (2016) apply a Kalman filter 2 to extract cycles from financial time series data for the U.S. and the Euro area. They find that series including credit, credit-to-GDP and real house prices demonstrate mediumterm cyclical behaviour and that the periodicity and amplitude of these cycles vary over time and between countries.…”

Section: Introductionmentioning

confidence: 99%

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Filter selection for countercyclical capital buffers

Visser

Vuuren

2018

SAJEMS

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Background: Procyclicality plays a pivotal role in finance in both thriving and crisis periods. This influence stems not only from the way market participants behave but also from risk metrics used and regulatory capital amassed and released during bust and boom periods, respectively. The introduction of the regulatory Countercyclical Capital Buffer aims to thwart procyclicality by accumulating (releasing) capital in upswings (downswings), subsequently reducing the amplitude of the financial cycle and promoting macroprudential stability. The timing of the accumulation and release of buffer capital is critical so identifying accurate indicators is important. Aim: This paper applies a Kalman filter to South African data and confirms the procyclicality of the Basel Committee on Banking Supervision (BCBS) proposal. Setting: For South Africa, studies suggest alternatives such as residential property indices because research has demonstrated that the BCBS proposal is procyclical rather than countercyclical. Methods: This paper applies a Kalman filter to South African data and compares the results obtained with those filtered using the Hodrick–Prescott filter. Results: Results indicate that buffer signals are dependent upon the filter employed. Conclusion: Buffer signals are strongly dependent upon the filter employed to detect procyclicality. The South African Reserve Bank and other regulators should reconsider the use of the Hodrick–Prescott filter and entertain the possibility of using the Kalman filter instead.

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