Statistical Methods in Finance 2019

Dec 16 - 21, 2019


Financial Time Series Forecasting using Statistical Methods: A Comparative Study

by Syamala Krishnakumar, University of Zululand, South Africa

In recent years modelling nonstationary-nonlinear financial time series has become a major challenge in the field of scientific research in Finance. Among statistical models ARIMA and GARCH are the most popular models that have been used in the analysis of financial time series. All the models have its own merits and de-merits. Recently, wavelets transforms and singular spectrum analysis have gained very high attention in many fields and applications such as physics, engineering, signal processing and applied mathematics. In this paper, the advantage and the disadvantages of wavelet transforms and singular spectrum analysis based models in forecasting financial time series data is compared.