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Dating the financial cycle with uncertainty estimates: a wavelet proposition

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  • Ardila, Diego
  • Sornette, Didier

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.

Suggested Citation

  • Ardila, Diego & Sornette, Didier, 2016. "Dating the financial cycle with uncertainty estimates: a wavelet proposition," Finance Research Letters, Elsevier, vol. 19(C), pages 298-304.
    • Handle: RePEc:eee:finlet:v:19:y:2016:i:c:p:298-304
    DOI: 10.1016/j.frl.2016.09.004
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    Cited by:

    1. Strohsal, Till & Proaño, Christian R. & Wolters, Jürgen, 2019. "Characterizing the financial cycle: Evidence from a frequency domain analysis," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 568-591.
    2. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    3. Saulius Jokubaitis & Dmitrij Celov, 2022. "Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition," Papers 2206.14128, arXiv.org.
    4. Greg Farrell & Esti Kemp, 2020. "Measuring the Financial Cycle in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 88(2), pages 123-144, June.
    5. Martin Mandler & Michael Scharnagl, 2022. "Financial Cycles in Euro Area Economies: A Cross‐Country Perspective Using Wavelet Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 569-593, June.
    6. C. Colther & J. L. Rojo & R. Hornero, 2022. "A Wavelet Method for Detecting Turning Points in the Business Cycle," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 171-187, July.
    7. Tran, Thuy Nhung, 2022. "The Volatility of the Stock Market and Financial Cycle: GARCH Family Models," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 151-168.
    8. Dalia Mansour-Ibrahim, 2023. "Are the Eurozone Financial and Business Cycles Convergent Across Time and Frequency?," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 389-427, January.
    9. Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.

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    More about this item

    Keywords

    Financial cycle; Wavelet transform; Multi-scale analysis; BBQ algorithm; Turning points; Interval estimates;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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