i. Providing a sound background in multiple time series analysis, state-of-the-art volatility modeling and econometric models for high-frequency data

ii. Implementing econometric theory using real financial data

iii. Practicing programing in R

iv. Evaluating and validating empirical research

1. Vector Autoregressive Processes

1.1. Basic Concepts

1.2. Vector Autoregressive Processes

1.3. Structural Analysis

1.4. Estimation and Diagnostics

1.5. Examples

2. Cointegrated Processes

2.1. Integrated Processes

2.2. The Concept of Cointegration

2.3. Cointegrated VAR Models

2.4. Examples

3. Nonlinear Time Series Models

3.1. TAR and STAR Models

3.2. Markov Regime Switching Models

3.3. Nonparametric Estimation

4. GARCH and Stochastic Volatility Models

4.1. Univariate GARCH Models

4.2. Multivariate GARCH Models

4.3. Stochastic Volatility Models

4.4. Estimation of Stochastic Volatility Models

5. High-Frequency Based Volatility Estimation

5.1. Realized Volatility

5.2. Estimating Volatility Under the Presence of Noise

5.3. Bi-Power Variation and Jumps

5.4. Covariance Estimation

6. Models for High-Frequency Financial Data

6.1. Financial Transaction Data

6.2. Dynamic Point Process Models

6.3. Models of the Trading Process