Provides an understanding of the methodology and techniques used in empirical research, enabling students to do independent research. Analysis and criticism of current empirical literature.
The aim of the paper is to acquaint the students with advanced empirical research methods in finance. Major topics covered include econometric theory, Box-Jenkins seasonal and non-seasonal modelling, unit root tests, cointegration and error correction models, vector autoregressive model (VAR), ARCH/GARCH, simultaneous equation model and modelling with panel data. The statistical package SAS (v.8) will be used intensively in the paper, and students are expected to write SAS programs on their own in analysing the data. An introductory lecture on SAS programming will be given at the beginning of the paper to teach the basics of SAS programming, and other procedures will be introduced during the lectures.
|Paper title||Advanced Empirical Finance|
|Teaching period||Second Semester|
|Domestic Tuition Fees (NZD)||$1,058.71|
|International Tuition Fees (NZD)||$4,692.94|
- Recommended Preparation
- FINC 308 and two further 300-level FINC papers
- Teaching staff
- Associate Professor I.M.Premachanra
- Paper Structure
- SAS programming.
- Teaching Arrangements
- Lectures and labs.
- Econometric models and Economic Forecasts (4th ed) by Robert S. Pindyck, Daniel L. Rubinfeld, McGraw-Hill (out of print) - available in the library
- Course outline
- View the course outline for FINC 406
- Graduate Attributes Emphasised
- Communication, Critical thinking, Research, Self-motivation.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Upon successful completion of this paper, you should be able to develop an understanding
of advanced econometric and time-series techniques used in analysing financial
data. At the completion of this paper students should be able to:
- Demonstrate knowledge of econometric and time-series techniques and their applications in finance
- Acquire data-analysis and programming skills using the statistical package SAS
- Get experience in extracting financial data from databases, such as Bloomberg, CRSP, and analysing them using SAS
- Acquire report writing and presentation skills