Магистратура 2020/2021

Введение в язык R и его применение в финансовом моделировании

Статус: Курс по выбору (Финансовая экономика)

Направление: 38.04.01. Экономика

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Когда читается: 1-й курс, 3 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Демешев Борис Борисович, Фантаццини Деан

Прогр. обучения: Финансовая экономика

Язык: английский

Кредиты: 3

Контактные часы: 36

Course Syllabus

Abstract

The goal of this course is to introduce R programming for financial applications, focusing on Bayesian Methods, Big Data analysis, Volatility Modelling, Market Risk Management, Option Pricing and Portfolio Optimization. The course wants to bridge the gap between theory and practice and the applied aspects of financial models are emphasized throughout the course. The practical part contains many realworld cases for which R is an indispensable tool. Pre-requisites: We assume that the students have a background in statistics and econometrics. An introduction to the basic concepts of financial modelling will be provided.

Learning Objectives

• At the conclusion of the course, students should be able to have:  Capability of self-development of new research methods, changing the scientific and production profile of activities
•  Ability to use modern information technologies and software in professional activities, to set tasks for specialists in the development of R software for solving professional problems.
•  Ability to prepare analytical materials for the assessment of economic policy and strategic decisionmaking at the micro-and macro-level.
•  Ability to forecast the main socio-economic indicators of the enterprise, industry, region and the economy as a whole.
•  Ability to make economic and financial organizational and managerial decisions in professional activities

Expected Learning Outcomes

• be able to use the basic data types in R and to manipulate them
• create advance graphics with ggplot2
• be able to implement basic techniques like OLS or logit
• be able to implement the basics of Bayesian methods in R
• handle and clean large datasets with R
• compute market risk measures in R
• be able to backtest risk measures with R
• be able to compute Mean-Variance optimal portfolios with R
• be able to compute risk optimal portfolios with R

Course Contents

• Data manipulation with R
  1.1 Fantastic data manipulation with dplyr 1.2. Chef d’œuvre using grammar of graphics of ggplot2 1.3. Basic techniques like ols or logit
• Time series with ARIMA
  2.1 ARIMA 2.2 Seasonal ARIMA 2.3 ARIMA with regressors
• Time series with ETS/UCM
  3.1 ETS model 3.2 UCM model
• Reporting with R
  4.1. R + Latex 4.2. R + Markdown
• R methods for Volatility Modelling and Market Risk Management
  1.1 Risk measures 1.2 Univariate GARCHmodels 1.3 Multivariate GARCHmodels 1.4 Value at Risk using GARCH models 1.5 Backtesting VaRestimates 1.6 Realized Volatility (*time permitting)
• R methods for Portfolio Management
  2.1 Introduction to Markowitz portfolio theory 2.2Mean-Variance portfolio: implementation in R 2.3Markowitz tangency portfolio and Long-only portfolio frontier 2.4Portfolio management using the R fPortfolio package 2.5Empirical Case study: Dow Jones index
• R methods for Credit Risk Management
  An introduction to classical credit risk management Credit risk for Small and Medium-sized Enterprises (SMEs): the case of crypto-exchanges and crypto-currencies Forecasting the Probability of Default (PD) of exchanges: Expert and credit rating systems Forecasting the Probability of Default (PD) of exchanges: Credit Scoring Systems Forecasting the Probability of Default (PD) of exchanges: Machine learning Model Evaluation: ROC, AUC and Loss Functions Credit Risk for quoted firms: Merton’s Model and the Zero Price Probability (ZPP) (*) (*) If time permits

Assessment Elements

• take-home assessment
• final exam

Interim Assessment

• Interim assessment (3 module)
  0.3 * final exam + 0.7 * take-home assessment

Bibliography

Recommended Core Bibliography

• Andrew Ellis, Yohan Chalabi, Rmetrics Packages, Diethelm Würtz, & William Chen. (2009). Portfolio Optimization with R/Rmetrics Rmetrics Association & Finance Online. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1F1CBB2F

Recommended Additional Bibliography

• Alexander J. McNeil, Rüdiger Frey, & Paul Embrechts. (2015). Quantitative Risk Management: Concepts, Techniques and Tools Revised edition. Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.pup.pbooks.10496