Paper Title
WAVELET COHERENCY TO CHARACTERIZE THE CYCLICAL NATURE OF THE SEMICONDUCTOR INDUSTRY

Abstract
Abstract - 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 cross-wavelet 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