Магистратура
2020/2021
Финансовая эконометрика
Статус:
Курс обязательный (Финансовая экономика)
Направление:
38.04.01. Экономика
Кто читает:
Международный институт экономики и финансов
Где читается:
Международный институт экономики и финансов
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Прогр. обучения:
Финансовая экономика
Язык:
английский
Кредиты:
6
Контактные часы:
60
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 become familiar with stylized facts of financial time series and get an overview of main questions in applied finance literature
- Students will learn the standard methodology of event studies and will be able to design and conduct the event studies on their own.
- 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 be able to model and estimate situations in which an economy can be in multiple regimes
- 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 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 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 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.
Course Contents
- Stylized facts of financial returns and sources of financial data.a. Stylized facts of the stock market returns: predictability, distribution, factor structure, CAPM b. Stylized facts on the bond returns and yield curve: predictability, yields, NelsonSiegel curves c. Simulation-based analysis and derivatives: example of mortgage-backed securities.
- Event studiesa. Methodology of the event studies b. Event studies in consulting: fraud on the market cases
- Tests of return predictabilitya. Forecast selection and comparison b. Interpreting predictability, Campbell-Shiller decomposition, forecasting with persistent predictors c. Performance evaluation: trading strategies and mutual funds
- Markov switching modelFormulation of Markov switching model, properties, estimation, filtered and smoothed probabilities.
- Kalman filterAsset pricing with time varying parameters.
- Volatility modelinga. Volatility clustering, ARCH and GARCH b. Asymmetric extensions to GARCH models
- Cross-sectional asset pricinga. Tests of CAPM and stock characteristics, Fama-MacBeth regressions b. Generalized Method of Moments c. Testing asset pricing models with GMM: SDF and linear factor models d. Pitfalls of cross-sectional asset pricing: identification, p-hacking, etc
- Forecasting in big data environmenta. Dimension reduction with Principal Components b. Forecasting with many predictors: sparsity and lasso
