Кто читает:: Международный институт экономики и финансов

Статус:: Курс обязательный

Когда читается:: 2-й курс, 1, 2 модуль

Преподаватели

Буданова Софья Ильинична

Киприянов Алексей Алексеевич

Full 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 Exam
Students should get at least 35 points for the final exam to pass the course.
Quizzes
Group project
Home assignment

Interim Assessment

2024/2025 2nd module
0.6 * Final Exam + 0.2 * Group project + 0.15 * Home assignment + 0.05 * Quizzes

Bibliography

Recommended Core Bibliography

Asset pricing, Cochrane, J. H., 2005
The econometrics of financial markets, Campbell, J. Y., 1997

Recommended Additional Bibliography

Analysis of financial time series, Tsay, R. S., 2010
Applied econometric time series, Enders, W., 2004

Authors

BUDANOVA SOFYA ILINICHNA

Магистерская программа «Финансовая экономика»

Financial Econometrics