IAM526 TIME SERIES APPLIED TO FINANCE

Course Objectives

The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction to the theoretical foundation of time series models. Much of statistical methodology is concerned with models in which the observations are assumed to be independent.  However, many data sets occur in the form of time series where observations are dependent.  In this course, we will concentrate on univariate time series analysis, with a balance between theory and applications.  In order to emphasize the application of theory to real (or simulated) data, we will use R. 

NOTE: Students must have basic-level statistics knowledge such as taking the expectation of a given process, finding the covariance function, and hypothesis testing concepts.

Course Content

This course introduces time series methodology emphasizing the data analytic aspects related to financial applications. Topics that will be discussed are as follows: Univariate linear stochastic models: ARMA and ARIMA models building and forecasting using these models. Univariate non-linear stochastic models: Stochastic variance models, ARCH processes and other non-linear univariate models. Topics in the multivariate modeling of financial time series. Applications of these techniques to finance such as time series modeling of equity returns, trading day effects and volatility estimations will be discussed.

Course Learning Outcomes

Student who completes this course sucessfully

* will have solved a reasonable number of exercises on classical time series models,

* find research texts (books / articles) using time series models more accessible,

* may get involved in applied research making use of basic time series models,

* will have a reasonable background to study more advanced texts and models.