Dating the Financial Cycle: A Wavelet Proposition

Diego Ardila and Didier Sornette
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Diego Ardila: ETH Zurich
Didier Sornette: Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)

No 16-29, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We propose to date and analyze the financial cycle using the Maximum Overlap Discrete Wavelet Transform (MODWT). Our presentation points out limitations of the methods derived from the classical business cycle literature, while stressing their connection with wavelet analysis. The fundamental time-frequency uncertainty principle imposes replacing point estimates of turning points by interval estimates, which are themselves function of the scale of the analysis. We use financial time series from 19 OECD countries to illustrate the applicability of the tool.

Keywords: Financial cycle; wavelet transform; multi-scale analysis; BBQ algorithm; turning points; interval estimates (search for similar items in EconPapers)
JEL-codes: C40 E30 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2016-04, Revised 2016-05
New Economics Papers: this item is included in nep-ets and nep-mac
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1629

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