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Магистратура 2019/2020

Введение в язык R и его применение в финансовом моделировании

Статус: Курс по выбору (Финансовая экономика)
Направление: 38.04.01. Экономика
Когда читается: 2-й курс, 2 семестр
Формат изучения: Blended
Прогр. обучения: Финансовая экономика
Язык: английский
Кредиты: 3

Программа дисциплины


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.
Цель освоения дисциплины

Цель освоения дисциплины

  • 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
Результаты освоения дисциплины

Результаты освоения дисциплины

  • 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
  • identify PnL opportunities, to analyse risks of derivatives portfolio, to evaluate collateral due to regulatory requirements, to hedge deltas and gammas
  • be able to price and hedge exotic options under the volatility surface with R.
  • implement in R Neural Networks (NN), Support Vector Machines (SVM), k-means clustering, and the Gaussian kernel clustering algorithm
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Introduction to R
    1.1 Basic data types (vector, data frame, list) 1.2 Fantastic data manipulation with dplyr 1.3 Long and wide tables with reshape2 1.4 Chef d’œuvre using grammar of graphics of ggplot2 1.5 Basic techniques like ols or logit
  • Bayesian approach with MCMC
    2.1 Bayesian approach, Markov chain Monte-carlo: theory 2.2Toy-example of MCMC with R 2.3Ready for use solutions in MCMCpack 2.4latest hit: spike and slab regression
  • Ideas for big data
    3.1 Fast data manipulation with data.table 3.2Use all the cores of your notebook with doMC (mac or linux only) 3.3Use Amazon EC2 if time permits
  • 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
  • Advanced Derivatives Analysis with R
    1.1 Storing options data in R 1.2 Volatility surface construction and prediction 1.3 Interactive 2d PnL plots of options portfolio in R 1.4 Strategies neutralizing greeks: Delta, Vega, Gamma, Vanna, Volga 1.5 Pricing with parametric models: local vol, SABR, and Vanna-Volga
  • Machine Learning in R
    2.1 Problems where machine learning techniques are applied 2.2Artificial NeuralNetworks 2.3Support VectorMachines 2.4Clustering algorithms: k-means, k-means++, Gaussian kernel
Элементы контроля

Элементы контроля

  • take-home assessment (неблокирующий)
  • final exam (неблокирующий)
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (II семестр)
    0.3 * final exam + 0.7 * take-home assessment
Список литературы

Список литературы

Рекомендуемая основная литература

  • 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
  • Lantz, B. (2013). Machine Learning with R : Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight Into Real-world Applications. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=656222
  • Paul Embrechts, Chapter In P. Embrechts, R. Frey, & A. Mcneil. (2004). Stochastic Methods for Quantitative Risk Management. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E3A771C1
  • Rebonato, R. (2004). Volatility and Correlation : The Perfect Hedger and the Fox (Vol. 2nd ed). Chichester, West Sussex, England: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=130884
  • Usuelli, M. (2014). R Machine Learning Essentials. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=918191

Рекомендуемая дополнительная литература

  • Gatheral, J. (2006). The Volatility Surface : A Practitioner’s Guide. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=170545