Time Series Analysis with R

In Chapter 3, we introduce time series analysis with R. In the financial service industry, we often must analyze various types of data, such as interest rates, stock prices, foreign currency rates. These data are not static, but evolve in time. We would like to understand how market data evolve in time, independently as well as together with other signals. Ideally, we would like to be able to forecast the future movement of these processes. Using time series analysis, we can make models to forecast financial times series. We discuss the method to collect historical financial market data, and how to test the stationarity of financial time series data. We then introduce various predictive models such as AR, ARMA, ARIMA, vector auto regressive, and GARCH models. Finally, we introduce cointegration, and demonstrate the application of time series analysis to pair trading.