Quantitative Finance and Insurance

This course provides an introduction to the econometric techniques used for the analysis of economic and financial time series. The course begins with an overview of ARMA and VARMA models, and then focuses on the nonlinear time series model used for the analysis of financial time series. In particular, the course will introduce the univariate nonlinear time series models used for the analysis of financial volatility (GARCH models) and the multivariate nonlinear models for the analysis of correlations (DCC models). 

 

Students are expected to learn how to use the economeetric models discussed and to conduct individually analyses of economic and financial data.

 

0. User's Guide

 

 1. Intro to Times Series

1.1. Time Series in Economics and Finance

1.2. Time Series as Stochastic Processes

1.3. Time Series Properties

 

2. Univariate Linear Time Series (ARMA)

2.1. Linear Time Series: Models

2.2. Linear Time Series: Prediction

2.2 Linear Time Series: Estimation

2.4. Linear Time Series: Practice

 

3. Multivariate Linear Time Series (VARMA)

3.1. Linear Time Series: Models

3.2. Linear Time Series: Prediction

3.3. Linear Time Series: Estimation

3.4. Linear Time Series: Practice

 

4. Volatility Modeling

4.1. Volatility Modeling: ARCH and GARCH

4.2. Volatility Modeling: Asymmetric Effects

4.3. Volatility Modeling: Prediction and Evalution

4.4. *Volatility Modeling: Stochastic Volatility

4.5. *Volatility Modeling: High Frequency Data Based Volatility Modelling

 

5. Covariance Modeling

5.1. Multivariate Volatility Models

 

6. Nonlinear Time Series Models

6.1 TVAR

6.2 STVAR

6.3 TVC-VAR

 

The course consists of 48 lecture hours. Strong interaction between teachers and students is encouraged. Part of the course   will be given at the Computer Lab.

- The exam consists of writing a short paper and presenting the work in a 20-minute presentation.

Analysis of Financial Time Series (13rd Edition). Ruey S. Tsay

Time Series Analysis. James D. Hamilton