Wavelet Improvement in Turning Point Detection Using a Hidden Markov Model

23 Pages Posted: 27 Mar 2014

See all articles by Yushu Li

Yushu Li

Norwegian School of Economics (NHH) - Department of Business and Management Science

Simon Reese

Lund University - Department of Economics

Date Written: March 25, 2014

Abstract

The Hidden Markov Model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice.

Keywords: HMM, turning point, wavelet, wavelet power spectrum, outlier

JEL Classification: C22, C38, C63

Suggested Citation

Li, Yushu and Reese, Simon, Wavelet Improvement in Turning Point Detection Using a Hidden Markov Model (March 25, 2014). NHH Dept. of Business and Management Science Discussion Paper No. 2014/10. Available at SSRN: https://ssrn.com/abstract=2416249 or http://dx.doi.org/10.2139/ssrn.2416249

Yushu Li (Contact Author)

Norwegian School of Economics (NHH) - Department of Business and Management Science ( email )

Helleveien 30
Bergen, NO-5045
Norway

Simon Reese

Lund University - Department of Economics ( email )

P.O. Box 7082
S-220 07 Lund
Sweden

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