Recently, there has been much work on modeling the series data, especially in financial markets. The purposes of time series analysis are to understand or model the stochastic mechanism, which gives rise to an observed series, and to predict or forecast the future values of a series based on the history of that series. Generally, there are two primary methods in series analysis, which are frequency-domain and time-domain. The former is based mostly on Fourier Transform while the latter closely investigates the autocorrelation of the time series. Time domain series make huge use of Box-Jenkins and ARCH/GARCH methods to perform forecast of the series.
In this project, this research generally investigates the financial time series such as the price and return of NASDAQ Composite Index using Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) methods.
Deatail can be found in Time Series Analysis with ARIMA:GARCH Model.pdf.
Here are the files of my final project of MA6609 Statistical Methods for Financial DATA.