Master
2021/2022
R Programming and Applications to Finance
Type:
Elective course (Financial Economics)
Area of studies:
Economics
Delivered by:
International College of Economics and Finance
When:
2 year, 3 module
Mode of studies:
distance learning
Online hours:
10
Open to:
students of one campus
Master’s programme:
Financial Economics
Language:
English
ECTS credits:
3
Contact hours:
32
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
- 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 backtest risk measures with R
- be able to compute Mean-Variance optimal portfolios with R
- be able to compute risk optimal portfolios with R
- be able to implement basic techniques like OLS or logit
- be able to implement the basics of Bayesian methods in R
- be able to use the basic data types in R and to manipulate them
- compute market risk measures in R
- create advance graphics with ggplot2
- handle and clean large datasets with R
Course Contents
- Data manipulation with R
- Time series with ARIMA
- Time series with ETS/UCM
- Reporting with R
- R methods for Volatility Modelling and Market Risk Management
- R methods for Portfolio Management
- R methods for Credit Risk Management
Assessment Elements
- take-home assessmentThe written exam is blocking, but if the take-home exam = 0, both the written exam and the take-home exam are subject to retakes
- final exam
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
- Quantitative finance with R and cryptocurrencies, Fantazzini, D., 2019