University of Southern California

Marshall School of Business

FBE 543 Forecasting and Risk Analysis
Summer 2014

Prof. Safarzadeh
Email: safarzad@marshall.usc.edu
Office Hours: W 5:00 - 6:00 pm, KAP 116B

Course Objectives

FBE 543 is an advanced Finance elective course that aims to develop the econometric tools used in many
practical problems of modern economics and finance. The quantitative tools developed in this course will
enable practitioners to estimate various asset-pricing models and obtain estimates of asset return correlations
and volatility. The course will require the use of theory and computer applications, with a bias toward the latter.
I will assume students are familiar with basic statistics and finance concepts. In addition to the tools of trade,
the course will also provide an up-to-date evaluation of the empirical evidence on asset pricing to guide
practitioners choice of investment strategies. We will cover two main statistical tools, (i) the Classical Multiple
Regression Model, (ii) Time Series Econometrics.

Required Course Material
Required textbook for the course is, Introductory Econometrics for Finance, 2
nd
edition by Chris Brooks,
Cambridge University Press, 2010, ISBN: 978-0-521-69468-1.This textbook is to serve as the point of departure
for lectures and some of the homework exercises and tests. A recommended textbook for those interested in a
more rigorous approach and understanding of time series analysis is, Applied Econometric Time series, 3
rd

edition by Walter Enders, Wiley, 2010, ISBN-13: 978-0470-50539-7. As well, you have to have access to
EViews by Quantitative Micro Software; http://www.eviews.com, or any other statistical software such as Stata,
SAS or SPSS for some of the hands-on practice in the class or doing the assignments.

Grading Policy
The course grade will be computed based on the following table.

Class attendance and participation 5%
Assignments (4, each 5 points) 20%
Course project and report 20%
Midterm 25%
Final Exam 30%

Class Attendance and participation
To familiarize myself with your names, each class meeting I will call the names of a few students randomly.
Students who receive three no shows during the random check will lose 5% attendance credit, unless they
provide a legitimate excuse for missing the classes that can be documented and verified. To earn 5% credit for
class participation, you may participate in class activities by answering questions, solving problems on the
board, sharing your data analysis and statistical work in the class or by reporting and discussing your applied
work in the class.

Homework Assignments
There will be four homework assignments each worth 5% of the course grade. Each homework assignment
involves the use of EViews or any other statistical software that you are familiar with on economic and financial
data. Completed homework assignments should be returned to me in the class, on time. There will be a penalty
for late submission of the homework. Some of the questions in the mid-term and the final exam will be similar
to the homework assignments. Therefore, I highly recommend that you work on the assignments diligently and
learn from them.

Course Project and Report
You are required to work on one applied project. The project will be part theory part practice. It will
concentrate on the application of the techniques taught during the semester to a topic of your own interest in the
area of forecasting and/or risk analysis. Choose a topic, review the relevant literature, build a model, collect
data for the variables, and apply the techniques as the course proceeds. You are required to report a summary of
your work and the results as they progress. The idea behind this assignment is to do a hands-on practice on
quantitative techniques after reviewing the relevant theoretical literature. The project will be worth 20 points
and will be graded as any test is graded. You have to show your knowledge of the subject matter as well as the
skills in applying the quantitative methods in analyzing and explaining the subject. At the end of the semester,
you are required to present the results in the class. Some of the suggested topics are: risk and return analysis,
tests of efficient market hypothesis applied to exchange markets or stock markets, application of forecasting
methods to economic variables or to financial markets, application of multiple regression in demand analysis,
elasticity estimations and optimum advertising, the relationship between macroeconomic variables such money
growth, exchange rate, interest rate and financial markets, cointegration of financial markets, bubbles and
structural breaks, intervention or transfer function analysis applied to financial markets, and so on. You are
required to submit your names and the topic of interest no later than the third week of the semester.

Midterm Exam
There will be one midterm exam during the course of the semester and a final exam. The midterm will be worth
25% of the course grade and will test all the material covered up to the exam. If you miss the exam for any
reason other than medical emergency, a score of zero will be assigned to the exam. If you miss the exam on
account of a proven medical emergency a makeup exam should be arranged as soon as the medical emergency
is over.

Final Exam
The final exam will be comprehensive but will emphasize the material covered after the midterm exam. The
final exam will be worth 25% of the course grade. If you miss the final exam for a medical emergency reason
that can be documented and verified, there will be a makeup final to be arranged as soon as possible.
Otherwise, a grade of zero will be assigned to the final exam. All the exams in this course are closed notes and
closed book.

Academic Integrity
Students are expected to adhere to the students academic integrity that governs students registered at USC.
Make yourself familiar with the University Student Conduct Code as described in Scampus 11-12. Where a
clear violation of the stated code has occurred, the students work will be disqualified, a failing grade will be
assigned, and a disciplinary action will be recommended.
The Use of unauthorized material, communication with fellow students during an examination,
attempting to benefit from the work of another student, and similar behavior that defeats the intent of
an examination, or other class work is unacceptable to the University. It is often difficult to distinguish
between a culpable act and inadvertent behavior resulting from the nervous tensions accompanying
examinations. Where a clear violation has occurred, however, the instructor may disqualify the
students work as unacceptable and assign a failing mark on the paper. (SCampus)

Statement for Students with Disabilities
Any student requesting academic accommodations based on a disability is required to register with Disability
Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be
obtained from DSP. Please be sure the letter is delivered to me as early in the semester as possible. DSP is
located in STU 301 and is open 8:30 a.m. 5:00 p.m., Monday through Friday. The phone number for DSP is
(213) 740-0776.

Getting Help
If you have questions concerning the lecture material or having problem understanding a concept or probably having
time for some intellectual chat on economic or technical issues, please feel free to drop in my office. If my office
hours are not convenient for you, email and make an appointment. For short questions, you may e-mail me at
safarzad@marshall.usc.edu.

Course Outline
The following course outline will be followed in a lecture format, but with sufficient flexibility to alter allotted time
and emphasis as questions arise. From time to time, class will be conducted on a discussion, problem solving, or lab
format. Regardless of which format is employed, questions and comments are encouraged.

Part I - Preliminary Concepts:
1. Review of mathematical concepts
2. Review of statistical concepts
3. Review of computer software: E-Views
4. Data sources, data collection, and data analysis

Part II Classical Regression Model, Chapters 1 4
1. Review of the Classical Linear Regression Model
2. Developments and Analysis of the Classical Linear Regression Model
3. Classical Linear Regression Model Assumptions and Diagnostic Tests
4. Qualitative variables and tests of structural breaks
5. Causality Test
46. Forecasting with and Application of the Classical Regression Model

Part III Smoothing Techniques (Notes)
1. Decomposing
2. MA, CMA, WMA, ES
3. Kalman Filter, Hodrick-Prescott Filter
4. Forecasting with and Application of Smoothing Techniques

Part IV Univariate Time-Series Modeling and Forecasting, Chapters 5 and 8
1. Non-Stationary Variable and Unit Root Test
2. Models with Trends, Deterministic and Stochastic Trends, Removing Trends
3. ARIMA Models and Forecasting
4. Box-Jenkins Model Selection
5. Modeling Volatility, ARCH, GARCH, ARCH-M
6. Intervention Function Analysis
7. Transfer Function Analysis
8. Forecasting with and Applications of ARIMA Models

First Exam

Part IV Multi-equation Time-Series Models, Chapters 6 - 7
1. Simultaneous Equation Models
2. VAR Analysis
3. Impulse Response Function
4. Cointegration and Error-Correction Models
5. Nonlinear Time-Series Models
6. Regime Switching Models
7. Threshold Autoregressive Models
8. Applications in Finance

Final Exam

Data Sources:
www.globalfindata.com (global financial data)
www.economagic.com
www.banx.com (banking, interest rate, )
www.bea.doc.gov (labor data, macroeconomic data)
www.lib.umich.edu/libhome/documents.center/stats.html (surveys of consumers and consumer confidence)
www.imf.org (international monetary fund)
www.hussman.com (stock market, money supply, interest rate, )
www.federalreserve.gov (interest rate, foreign exchange rate, consumer credit, )