Financial Time Series: Theory and Computation

Course Introduction

This module introduces students to financial time series techniques, focusing primarily on ARIMA models, conditional volatility (ARCH/GARCH models), regime switching and nonlinear filtering, diverse nonlinear state models, co-integration, and their applications on real-life financial problems.

We provide both the relevant time series concepts and their financial applications. Potential applications of financial time series models include modeling equity returns, volatility estimations, Value at Risk modelling and option valuation. This module targets honours students in the Quantitative Finance Programme and students in the Master of Science in Quantitative Finance Programme.

Schedule

1. Intro : R, proba & stats
2. Linear regression
3. Logit and probit models
4. Regression models
5. Univariate time series models
6. ARIMA modelling
7. ARCH modelling
8. GARCH modelling
9. Nonlinear models
10. Risk measures and risk management
11. Risk measures and multivariate TS
12. Multivariate TS and co-integration
13. Review

Slides

• R Introduction

Books

• Analysis of Financial Time Series, 3rd Edition, Ruey S. Tsay, Wiley, 2010. Chapters 1-4, 7, 8, 10.

• Statistics and Data Analysis for Financial Engineering, David Ruppert, Springer, 2010. Chapters 2, 4, 5, 7, 9, 10, 12, 14, 15, 18, 19.

• Introductory Econometrics for Finance, 2nd Edition, Chris Brooks, Cambridge University Press, 2008.

Programming Language

• R Language + RStudio

Code

• Lecture1
• Lecture2
• Lecture3
• Lecture4: Quiz
• Lecture5
• Lecture6
• Lecture7: Quiz
• Lecture8
• Lecture9
• Lecture10
• Lecture11

Note:

1. Lecture1(the file is updated in April 2019): for digital asset, the APIs for getting cryptocurrency are unavailable for package coinmarketr from March 2020.