课程大纲

COURSE SYLLABUS

课程代码/名称

Course Code/Title

FIN5017

金融时间序列分析

Analysis of Financial Time Series

课程性质

Compulsory/Elective

选修课 Elective Course

课程学分/学时

Course Credit/Hours

3/48

授课语言

Teaching Language

中英双语 English & Chinese

授课教师

Instructor(s)

孙便霞 SUN Bianxia

是否面向本科生开放

Open to undergraduates

or not

是 Yes

先修要求

Pre-requisites

（如面向本科生开放，请注明区分内容。

If the course is open to

undergraduates, please indicate the difference.）

概率论与数理统计 Probability and Statistics MA212

教学目标

Course Objectives

此课程旨在讲授金融时间序列分析领域里的经典模型以及分析方法，使学生在掌握时间序列模型理论

内容的同时，学会对真实金融市场上的数据进行建模分析。同时，该课程也会介绍目前该领域里处于

研究前沿的相关内容和研究方向。

This course aims to teach students the classical models and analysis methods in the field of financial time

series. Besides mastering the theore

tical knowledge of time series models, students are also expected to be

capable of model

ling the time series data in real financial markets. In addition, this course also introduces

some related contents at the forefront of research in this field.

教学方法

Teaching Methods

本课程通过课堂讲授的方式讲解理论内容，同时时间序列模型的实际应用部分将结合

软件来进行。

The theoretical contents of this course will be instructed in details in class, and the software R will be used in

the real applications of time series models.

10.

教学内容

Course Contents

Section 1

Characteristics of financial time series data

In this chapter, students will learn about the characteristics of financial time

series data, mainly related to the distribution of asset returns, the stability of

time series data, and how to make white noise test. Students are expected to

have a better foundation for future learning of financial time series models

Section 2

Properties, estimate, and forecast of Moving Average models

This chapter will illustrate the simple financial econometric mod

el for

modelling asset returns. Students will learn the moving average model,

including weak stationary property, reversible property. The teacher

will

also explain how to identify the order of the moving average model by using

the autocorrelation function. Estimating the parameters of the model using

maximum likelihood estimation is quite essential. The teacher

will introduce

how to use the moving average model for prediction as well.

Section 3

Properties, estimate, and forecast of Auto-Regressive models

In this chapter, students will learn about the characteristics of AR

models.

They are expected to grasp the stationary condition of AR process,

the idea of

PACF, how to use the LS method

to estimate model’s parameters, and how to

test the model’s sufficiency of fitting the data through the Ljun-

Box statistic

test.

Section 4

Properties, estimate, and forecast of Auto-Regressive and Moving

Average models

his chapter will introduce the characteristics of the ARMA (1,1) model, and

the students will learn a

bout using the extended autocorrelation function

(EACF) to determine the order of the ARMA model, and how to use the

ARMA model for prediction.

Section 5

Non-stationary time series and unit root test

In this chapter, students will learn about the unit root non-

stationary time

series and the random walk time series with drift. At the same time,

the

teacher will introduce the time series with trend and the general unit root non-

stationary model, as well as the unit root test method.

Section 6

Multivariate time series and Vector Auto-Regressive models

In this chapter, students will learn about financial econometrics models for

studying multivariate time series. They are expected to

grasp the concept of

cross-correlation matrix and the simple commonly used

vector autoregressive

model (VAR). The teacher

will illustrate the form and stationary conditions of

the VAR(1) model, how to estimate the parameters and how to test for the

specified VAR model.

Section 7

Impulse response function and variance decomposition

This chapter will introduce the impulse response function and

variance

decomposition for a fitted vector autoregressive model. The teacher will

demonstrate how to analyze financial data and make predictions based on the

learned models through statistical software.

Section 8

Co-integration test and Error Correction Model

In this chapter, students will learn about the idea of co-integration, and co-

integrated VAR model estimations as well. The teacher will explain co-

integration test methods and error corrected models in details.

Section 9

Mid-term Review

Review all of the contents covered in the former half semester and make

students prepare for the mid-term exam.

Section 10

Properties, estimate, and forecast of ARCH models

In this chapter, stude

nts will begin to learn about the econometric models of

asset return volatilities. The teacher

will introduce the characteristics of

volatility, basic properties of the ARCH model,

the determination of the order

for ARCH model, the estimation of the parameters and the test of the model.

Section 11

Properties, estimate, and forecast of GARCH models

In this chapter, students will learn about a model that could more fully

describe the volatility process of assets’ return: the generalized autoregressive

conditional heteroskedasticity (GARCH) model. The teacher

will introduce the

basic properties of the GARCH model, what are the pros and cons of the

model, how to estimate the GARCH model by the two-

step estimation

method, and the prediction of the model.

Section 12

Properties, estimate, and forecast of asymmetric GARCH models

In this chapter, students will learn about asymmetric GARCH models. The

teacher will introduce the asymmetric effects of the EGARCH model on

positive and negative return of assets, the estimation methods of the model,

and how to use the asymmetric GARCH model for prediction.

Section 13

Volatility estimates based on high-frequency data and market

microstructure

In this chapter, students will learn about the unique characteristics of financial

high-frequency data and how to estimate the volatility based on high-

frequency data. Besides this, the teacher will introduce some core contents

related to market microstructure, such as asynchronous transactions, bid and

offer spread, and empirical characteristics of trading data.

Section 14

Risk measures and calculating methods of VaR

In this chapter, students will learn about the various ways to calculate the

value at risk (VaR). The teacher will introduce the concept of VaR, Risk

Metrics’s method and econometric methods of VaR calculation. The idea of

expected shortfall will also be covered.

Section 15

Introduction to factor models

In this chapter, students will learn the basic knowledge of macroeconomic

factor models, fundamental factor models, and statistical factor models. In the

part of fundamental factor models, the estimate methodology of BARRA

model will be introduced briefly.

Section 16

Project Presentation and Final Review

Students are required to present their project results in class. The teacher will

eview all of the contents covered in this course and make students prepare for

the final exam.

11.

课程考核

Course Assessment

（

○

1 考核形式 Form of examination；

○

2 .分数构成 grading policy；

○

3 如面向本科生开放，请注明区分内容。

If the course is open to undergraduates, please indicate the difference.）

15%平时作业 + 10% 小测验 + 15%期末报告 + 30%期中考试 + 40%期末考试

15% Assignments + 10% Quiz + 15% Final Report + 30% Midterm Exam + 30% Final Exam

12.

教材及其它参考资料

Textbook and Supplementary Readings

指定教材

Textbook

：

Ruey S. Tsay, Analysis of Financial Time Series, 3rd edition, Wiley, 2010.