<strong>Paper Title</strong><br>
Wavelet Coherency to Characterize The Cyclical Nature of The Semiconductor Industry<br>
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<strong>Abstract</strong><br>
The characterization of the cyclical nature of semiconductor industry is a complex endeavor because of the
presence of many interacting transient dynamics inherent in the industry’s ecosystem. In this paper we present a
methodology that addresses some of the issues, particularly the non-stationarity of the time series associated with the
semiconductor industry. We use singular spectrum analysis to de-noise data before identifying the dominant pattern of the
semiconductor stock market using singular value decomposition. By using continuous wavelet transformation and crosswavelet
coherence relation, the nexus between the dominant pattern of the stock market and the industrial production index
of semiconductor is established. Using a bootstrap resampling method, statistically significant frequencies that characterize
the cyclical nature of the semiconductor industry are identified.
Keywords - Wavelet analysis, Wavelet coherence, Semiconductor Industry, Singular Spectrum Analysis