Master
2024/2025
Financial Econometrics
Type:
Compulsory course (Financial Economics)
Area of studies:
Economics
Delivered by:
International College of Economics and Finance
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Financial Economics
Language:
English
ECTS credits:
6
Course Syllabus
Abstract
Financial Econometrics is a one-semester course taught to the second year students of the ICEF Master program in Financial Economics. It is designed to cover essential tools for working with financial data, including return forecasting, volatility and econometrics of asset pricing, such as testing the market models. We focus on the empirical techniques that are mostly used in the analysis of financial markets and on how they are applied to actual data. The course starts with an overview of the financial data. Then it covers the event-study methodology and continues with analyzing return predictability and the volatility effects of the market data (asymmetric GARCH). We then proceed to testing market models (Fama-McBeth regressions, etc.) and stochastic discount factor models. Other important topics can be covered subject to time availability. All the models are accompanied with real-data examples in standard computer packages. Course Pre-requisites: Mathematics for Economics and Finance, Financial Economics I (Asset pricing), Econometrics I-II.
Learning Objectives
- The main objectives of the course are to introduce the students to the modern methods of analysis of financial data and prepare them for individual work, in particular on their master's theses.
- Upon completion of the course students will be able to: • use event-study methodology in applied research;
- • forecast financial data using high-level econometric techniques and measure their effectiveness;
- • test the standard asset pricing models.
Expected Learning Outcomes
- Students will be able to model and estimate situations in which an economy can be in multiple regimes
- Students will be able to model dependence in conditional variance of times series data and become familiar with the concept of realized and implied volatility. They will be able to estimate the basic models of conditional heteroskedacticity using statistical software.
- Students will be able to model situations in which an economy can be in multiple states, with the state variables being unobserved. They will learn how to use the Kalman filter for financial and macroeconomic data
- Students will become familiar with stylized facts of financial time series and get an overview of main questions in applied finance literature
- Students will know specifics of forecasting when many potential predictors are available. They will be able to apply principal components analysis and several machine learning techniques to tackle such questions.
- Students will learn about factor analysis approach, and Fama-French factor models in particular. They will be able to test asset pricing models on the data.
- Students will learn different approaches to assessing predictive ability of models and how to apply them to answer the question of whether the financial returns are predictable or not.
- Students will learn the standard methodology of event studies and will be able to design and conduct the event studies on their own.
Course Contents
- Stylized facts of financial returns and sources of financial data.
- Event studies
- Tests of return predictability
- Markov switching model
- Kalman filter
- Volatility modeling
- Cross-sectional asset pricing
- Forecasting in big data environment
Assessment Elements
- Final ExamStudents should get at least 35 points for the final exam to pass the course.
- Quizzes
- Group project
- Home assignment
Interim Assessment
- 2024/2025 2nd module0.6 * Final Exam + 0.2 * Group project + 0.15 * Home assignment + 0.05 * Quizzes
