응용통계연구 (The Korean Journal of Applied Statistics)

  • 제22권3호
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  • Pages.499-513
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  • 2009
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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A Multi-Resolution Approach to Non-Stationary Financial Time Series Using the Hilbert-Huang Transform

  • 발행 : 2009.06.30
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초록

An economic signal in the real world usually reflects complex phenomena. One may have difficulty both extracting and interpreting information embedded in such a signal. A natural way to reduce complexity is to decompose the original signal into several simple components, and then analyze each component. Spectral analysis (Priestley, 1981) provides a tool to analyze such signals under the assumption that the time series is stationary. However when the signal is subject to non-stationary and nonlinear characteristics such as amplitude and frequency modulation along time scale, spectral analysis is not suitable. Huang et al. (1998b, 1999) proposed a data-adaptive decomposition method called empirical mode decomposition and then applied Hilbert spectral analysis to decomposed signals called intrinsic mode function. Huang et al. (1998b, 1999) named this two step procedure the Hilbert-Huang transform(HHT). Because of its robustness in the presence of nonlinearity and non-stationarity, HHT has been used in various fields. In this paper, we discuss the applications of the HHT and demonstrate its promising potential for non-stationary financial time series data provided through a Korean stock price index.

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참고문헌

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피인용 문헌

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  2. EMD based hybrid models to forecast the KOSPI vol.29, pp.3, 2016, https://doi.org/10.5351/KJAS.2016.29.3.525
  3. Impact Tension Damage Mechanism Analyses of Co-Woven-Knitted Composite from Hilbert–Huang Transform vol.21, pp.4, 2012, https://doi.org/10.1177/1056789511406759
  4. Empirical Mode Decomposition using the Second Derivative vol.26, pp.2, 2013, https://doi.org/10.5351/KJAS.2013.26.2.335