WEERACHART T. KILENTHONG
 ­­­­­Graduate School University of the Thai Chamber of Commerce SM512 StatisticalTheory M.Sc. in Financial Engineering First Semester of 2018

Course Syllabus

Instructor:                    Weerachart Kilenthong

Course Schedule:        Saturday at Room 5601

Email:                          tee@riped.utcc.ac.th

Website:                       riped.utcc.ac.th/tee/teaching/sm512

Teaching Assistance: Sartja Duangchaiyoosook and Wasinee Junton

Email: kei@riped.utcc.ac.th and wasinee_jun@riped.utcc.ac.th

# 1. Course Description

This course studies basic concepts of probability and statistical theory relevant to financial engineering. The topics include basic probability, conditional probability, random variables and their distributions, expectation and moments, special distributions, asymptotic theory and properties of large random samples, point estimation and maximum likelihood estimation, sampling distributions of estimators, hypothesis testing, linear statistical models, basic nonparametric methods. Advanced topics may include Markov chains both in discrete and continuous time models, basic Bayesian estimation methods and Kalman filtering.

# 2. Course Objective

The aim of this course is to give master-level students an introduction to principles, theories, and tools in advanced statistical theory. Students will also learn how to apply statistical models with real data using R software.

# 3. Required Textbooks:

1. DeGroot, Morris H. and Mark J. Schervish. 2012. Probability and Statistics. 4th edition: Preason. [DS]

2. Hogg, Robert V., Allen T. Craig and Joseph W. McKean. 2005. Introduction to Mathematical Statistics. 6th edition, Pearson. [HCM]

Optional Textbooks:

Data Sources

We will provide relevant data through the course website: riped.utcc.ac.th/tee/teaching/sm512

Program Sources

Grades will be based on the following weights:

30%         Assignment(s)

30%         Mid-Term Exam

40%         Final Exam

85 – 100   A

80 – 84     B+

70 – 79     B

65 – 69     C+

55 – 64     C

50 – 54     D+

40 – 49     D

39 or less F

## 4.1 Assignment

Students will be assigned to complete 8-10 individual assignments during the semester. An assignment with the lowest score will be dropped when calculating the total score for each student. Note: Late submission of the assignments is not accepted; a score of zero will be recorded for that assignment.

## 4.2 Examination

There will be two examinations: a mid-term exam counting for 30% of the total points, and a final exam counting for 40% of the total points. If a student misses a regular examination without acceptable excuse, a score of zero will be recorded for the examination.

Problem Assignments

1. Problem Assignment 1 (Due on August 25 at the beginning of the class),

2. Problem Assignment 2 (Due on September 1 at the beginning of the class),

3. Problem Assignment 3, data for problem set (Due on September 8 at the beginning of the class),

4. Problem Assignment 4 (Due on September 15 at the beginning of the class),

5. Problem Assignment 5,  data for problem set (Due on September 19 at the beginning of the class),

6. Problem Assignment 6,  data for problem set (Due on September 29 at the beginning of the class),

7. Problem Assignment 7,  data for problem set (Due on November 10 at the beginning of the class),

8. Problem Assignment 8 (Due on November 17 at the beginning of the class),

9. Problem Assignment 9,  data for problem set (Due on November 24 at the beginning of the class),

10. Problem Assignment 10,  data for problem set (Due on December 1 at the beginning of the class),

11. Problem Assignment 11,  data for problem set (Due on December 8 at the beginning of the class),

Course Schedule

The course will be carried out in 15 sessions, totalling 45 lecture hours. The structure of the course is subject to revision if necessary (e.g., to conform to the background, knowledge, and interests of the students). The tentative structure of the whole course is as follows:

 Week Topics Reading Materials Chp. 1-2 of DS and Lecture Note 2 Chp. 3 of DS and Lecture Note 3 Random Variables and Their Probability Distributions Chp. 3 of DS and Lecture Note 4 Random Variables and Their Probability Distributions Chp. 3 of DS and Lecture Note 5 Expectation and Moments Chp. 4 of DS and Lecture Note 6 Expectation and Moments Con't Chp. 4 of DS and Lecture Note 7 Special Distributions Chp. 5 of DS and Lecture Note Midterm Examination covering up to the 6th session Matrix and Special Distributions (Con' t) Chp. 5 of DS and Lecture Note 9 Asymptotic Theory and Large Random Samples Chp. 6 of DS and Lecture Note 10 Point Estimation and Maximum Likelihood Estimation Chp. 7 of DS and Lecture Note 11 Point Estimation and Maximum Likelihood Estimation ( Con’t ) Chp. 7 of DS and Lecture Note 12 Sampling Distributions of Estimators and Hypothesis Testing Chp. 8 and Chp. 9 of DS and Lecture Note 13 Linear Statistical Models Chp. 10 of DS and Lecture Note 14 conclusion_1 Chp. 10 of DS and Lecture Note 15 conclusion_2 Chp. 10 of DS and Lecture Note Final Exam covering from the 7th session to the 15th session