The course examines popular time series models that are often used to analyze financial data. Topics include linear, nonlinear, (quasi) maximum-likelihood, generalized method of moments, and quantile model estimation methods that are popularly used to analyze stationary time series data such as autoregressive and moving average (ARMA) process, autoregressive conditional heteroskedastic (ARCH) process and so one. The course first discusses the theoretical parts of these data and models and apply them to simulated or real data analysis using statistical software packages; non-stationary time series data analysis will be covered if time allows.