Last Updated:
10/03/2019 - 15:02

IAM526 - Time Series Applied To Finance

Credit: 3(3-0); ECTS: 8.0
Instructor(s): Ceylan Yozgatlıgil
Prerequisites: Consent of Instructor(s)

Course Catalogue Description

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 Objectives

The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into 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. After completing this course, a student will be able to analyze univariate time series data using available software as well as pursue research in this area. In order to emphasize application of theory to real (or simulated) data, we will use R or SAS.

Course Learning Outcomes

Student who completes this course successfully
  • 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

Tentative (Weekly) Outline

  1. Fundamental Concepts
  2. Properties of autocovariance and autocorrelation of time series
  3. Stationary and nonstationary time series models
  4. Time series modeling (identification, parameter estimation, and model selection)
  5. Seasonal time series models
  6. Time series forecasting
  7. Testing for a unit root
  8. Diagnostic Checking
  9. VAR models, Granger Causality
  10. Cointegration

Course Textbook(s)

  • D. C. Cryer and K. Chan, Time Series Analysis with Application in R, 2nd Edition, Springer, 2008.

Supplementary Materials and Resources

  • J. D. Cryer, Time Series Analysis, PWS-Kent Publishing Company, Boston, 1986.
  • William W.S Wei, Time Series Analysis, Univariate and Multivariate Methods, Second Edition, Addison-Wesley, 2006.
  • G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis, Forecasting and Control 3rd ed., Prentice-Hall, 1994.
  • C. Chatfield, The Analysis of Time Series, Sixth Edition Chapman & Hall/CRC, 2004.
  • D. Pena, G. C. Tiao, and R. S. Tsay, A Course in Time Series Analysis, Wiley Interscience, 2001.
  • Robert A. Yaffee, Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS, San Diego, Academic Press, 2000.
  • R. S. Tsay, Analysis of Financial Time Series, Wiley Interscience, 2002.

More Info on METU Catalogue

Back