Wavelet-based prediction of financial data

• Main Authors: Lu,i-shan, 呂宜珊
• Other Authors: LI, MENG-FENG
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/69675921817104331020

碩士 === 國立臺北大學 === 統計學系 === 98 === Regarding to the model building of financial products, many researchers applied regression method to build time series model for analysis and prediction. The regression model of time series data developed from the earliest Autoregressive model (AR model) to present the GARCH family. The ARIMA/GARCH family has been developed very large and complex. The regression models of time series data have to be built under the data being stationary. Since financial time series data are usually non-stationary, the original data need to be transformed to meet the stationary assumption before model building. Otherwise, other methods to build the finance time series have to be considered. This paper illustrates an application of wavelets as a possible method for prediction of financial data. One of the benefits of a wavelet approach is the flexibility in handling very irregular data series. In this study intended to use wavelet transform to do pre-processing on the finance time series data. The entire procedure can be roughly divided into three steps: wavelet decomposition, signal extension and wavelet reconstruction. The predicted results are compared with output from time series regression model. The most important property of wavelets for economic analysis is decomposition by time scale. Economic and financial systems, like many other systems, contain variables that operate on a variety of time scales simultaneously so that the relationships between variables may well differ across time scales.