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Analysis of non-stationary dynamics in the financial system

Author

Listed:
  • Guharay, Samar K.
  • Thakur, Gaurav S.
  • Goodman, Fred J.
  • Rosen, Scott L.
  • Houser, Daniel

Abstract

Novel data-driven analyses, appropriate for detecting economic instability in non-stationary time series, are developed using functional principal component analysis (fPCA) and Synchrosqueezing. fPCA is applied in a new way, aggregating multiple financial time series to identify periods of macroeconomic instability. Synchrosqueezing, a technique which generates a time-series’ time-dependent spectral decomposition, is modified to develop a new quantitative measure of local dynamical changes and structural breaks. The merit of this integrated technique is demonstrated by analyzing financial data from 1986 to 2012 that includes equity indices, securities and commodities, and foreign exchange. Both procedures successfully detect key historic periods of instability. Moreover, the results reveal distinctions between periods of long-term gradual change in addition to structural breaks. These tools offer new insights into the analysis of financial instability.

Suggested Citation

  • Guharay, Samar K. & Thakur, Gaurav S. & Goodman, Fred J. & Rosen, Scott L. & Houser, Daniel, 2013. "Analysis of non-stationary dynamics in the financial system," Economics Letters, Elsevier, vol. 121(3), pages 454-457.
  • Handle: RePEc:eee:ecolet:v:121:y:2013:i:3:p:454-457
    DOI: 10.1016/j.econlet.2013.09.026
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    References listed on IDEAS

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    1. Branch, William A. & Evans, George W., 2013. "Bubbles, crashes and risk," Economics Letters, Elsevier, vol. 120(2), pages 254-258.
    2. Lars Peter Hansen & José A. Scheinkman, 2009. "Long-Term Risk: An Operator Approach," Econometrica, Econometric Society, vol. 77(1), pages 177-234, January.
    3. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    4. Branch, William A. & Evans, George W., 2013. "Bubbles, crashes and risk," Economics Letters, Elsevier, vol. 120(2), pages 254-258.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    7. Khandani, Amir E. & Lo, Andrew W. & Merton, Robert C., 2013. "Systemic risk and the refinancing ratchet effect," Journal of Financial Economics, Elsevier, vol. 108(1), pages 29-45.
    8. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 255-296, October.
    9. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    10. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    11. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    12. Maldarella, Dario & Pareschi, Lorenzo, 2012. "Kinetic models for socio-economic dynamics of speculative markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 715-730.
    13. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    14. W.-X. Zhou & D. Sornette, 2007. "Self-organizing Ising model of financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 175-181, January.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Samar K. Guharay & Gaurav S. Thakur & Fred J. Goodman & Scott L. Rosen & Daniel Houser, 2016. "Integrated data-driven analytics to identify instability signatures in nonstationary financial time series," Applied Economics, Taylor & Francis Journals, vol. 48(18), pages 1678-1694, April.
    2. Alvaro Arroyo & Bruno Scalzo & Ljubisa Stankovic & Danilo P. Mandic, 2021. "Dynamic Portfolio Cuts: A Spectral Approach to Graph-Theoretic Diversification," Papers 2106.03417, arXiv.org.

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    More about this item

    Keywords

    Non-stationary time series; Functional PCA; Synchrosqueezing; Multi-time scale characteristics; Detection of macroeconomic instability;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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