Predicting Malaysia Business Cycle using Wavelet Analysis Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department, Universiti Teknologi Petronas, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan, Malaysia. E-mail: samsul_ariffin@petronas.com.my Bakri Abdul Karim Fakulti Ekonomi dan Perniagaan, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia. E-mail: akbakri@feb.unimas.my Fredrik NG Andersson Department of Economics, Lund University P.O. Box 7082 S-220 07 Lund, Sweden NGF.Andersson@nek.lu.se Mohammad Khatim Hasan Jabatan Komputeran Industri, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. khatim@ftsm.ukm.my Jumat Sulaiman Program Matematik dengan Ekonomi, Universiti Malaysia Sabah, Beg Berkunci 2073, 88999 Kota Kinabalu, Sabah, Malaysia. jumat@ums.edu.my Radzuan Razali Fundamental and Applied Sciences Department, Universiti Teknologi Petronas, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan, Malaysia. E-mail: radzuan_razali@petronas.com.my Abstract-Wavelet transforms are capable to decompose time series at various level which corresponds to the resolution of the decomposition. We can find the trend, cycle, noise, structural break etc. This is where wavelets are so efficient in studying characteristics of the any time series. In this present article, we study the use of wavelet (symlet 16) to detect the business cycle in Malaysia. Firstly we decompose the time series then we study the long-run trend and we filtered the high frequency components and finally we find the business cycle in Malaysia. The results indicated the existence of business cycles for GDP data in Malaysia which is strongly counter-cyclical. I. INTRODUCTION Wavelets are successfully being applied in various disciplines e.g. engineering, mathematics and sciences. One of the main advantages of wavelets is there exist fast algorithm to compute the coefficients. Furthermore with multi-resolution analysis (MRA), we can study the characteristics of the data at any level, hence this is why wavelets potentially being used in various economics and finance applications. For instance, [1] has studied the use of wavelets in financial applications. Reference [2] has utilized the multi-resolution property to show the business cycle for USA GDP data. He found that wavelets analysis provides better resolution in the time domain as compared with other band-pass filters that is always being used in economics. Reference [3] has studied the use of wavelet transform in stock exchange problem. They found that wavelets (symlet 8) are capable to analyze Kuala Lumpur Composite Index (KLCI) even at level 7. They also show that the Minimax and fixed form threshold give the better result as compared to the heuristic SURE and SURE options in terms of Root Mean Square Error (RMSE) calculation. Reference [4] has studied the applications of DWT in compressing the temperature data. In this paper they use Daubechies 4 (D4) to compress the original data at level 5. Meanwhile [5] has used wavelets together with Box-Jenkins ARIMA model to study the time series forecasting. References [6], [7], [8] has applied WT into various problem in finances and they also show that wavelets are really suitable to study the time series behavior. For more details on wavelet theory and its applications in various disciplines, the reader are encourage to refer [9], [10], [11], [12] – this books discuss about wavelets and other filtering method and its applications in finances and economics, [13] and [14]. Of course there exist various textbooks on wavelets in the markets. Noise is extraneous information in a signal or time series that can be filtered out via the computation of averaging and detailing coefficients from wavelet transforms. Hence we can filter out the low frequency (approximation coefficients) and high frequency (details coefficients). In fact many statistical phenomena have wavelet structure [3]. In this paper we will discuss the applications of wavelets in detecting the business cycle (BC) in Malaysia using Malaysia GDP data. We use the data from Quarter 1 year 1980 until Quarter 2 year 2007. Total we have 110 data. We utilized symlet 16 to filter the data and study the business cycle from the decomposition of the data via MRA. The reason we apply symlet 16 because it has 32 filters and after we have done several numerical simulation we noticed that symlet 16 is really suitable to find the business cycle in Malaysia. Furthermore, symlet 16 has higher vanishing 2011 IEEE Symposium on Business, Engineering and Industrial Applications (ISBEIA), Langkawi, Malaysia 978-1-4577-1549-5/11/$26.00 ©2011 IEEE 379