R Programming and Applications to Finance
- 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
- 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
- Data manipulation with R1.1 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 ARIMA2.1 ARIMA 2.2 Seasonal ARIMA 2.3 ARIMA with regressors
- Time series with ETS/UCM3.1 ETS model 3.2 UCM model
- Reporting with R4.1. R + Latex 4.2. R + Markdown
- R methods for Volatility Modelling and Market Risk Management1.1 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 Management2.1 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 ManagementAn 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
- 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
- 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