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 masterlevel 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
Program R version 3.5.1+RStudio
4. Grades and Requirements
Grades will be based on the following weights:
30% Assignment(s)
30% MidTerm Exam
40% Final Exam
Tentative Grading Range:
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 810 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 midterm 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. 12 of DS and Lecture Note 

2 
Random Variables and Their Probability Distributions lecture 
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 6^{th} 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 7^{th} session to the 15^{th} session 