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Teaching > Teaching materials MAFINRISK TOPICS IN FINANCIAL ECONOMETRICS with R. Part 1
Applied Macroeconometrics Notes on the Econometrics of Asset Allocation and Risk Measurement PhD supervision Curriculum Vitae Teaching Course syllabus Working papers
Topics in Financial Econometrics with R 1 MAFINRISK 2017-18 Instructors Fabio Piacenza (
[email protected]) Fabio Monti (
[email protected]) Anna Rita Filippi (
[email protected]) Matteo Ognibene (
[email protected]) Carlo Favero (
[email protected])
Publications
1. 2. 3. 4.
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Objective of the Course The Exam and Assessment Methods Textbooks and other support material Detailed Syllabus
1. Objective of the Course R is a free software environment for statistical computing and graphics (https://www.r-project.org/). This course is aimed at learning the R language using the R studio suite, with the goal of giving to participants the skills to tackle a quantitative problem that requires data management, also in case of large volumes, data description, model specification, estimation and simulation and present results with effective reporting. The course, for a total of 32 hours, is structured on 8 modules, with the involvement of the students who have to perform simple exercises directly on their laptops in the classroom, without explicit separation between theoretical and experiential learning. Classroom activity is complemented and supported by the interaction among students and teachers. 2. The Exam and Assessment Methods The exam wil be computer based. Students will have to comment and adapt codes that they have mastered during the course to provide econometric analysis of issues closely related to those that have been addressed in the sequence of class exercises. Working on the exercises is the best preparation strategy for the exam. During the exam students will be allowed to use all the relevant software programmes and notes 3. Textbooks and other support materials Software requirements: R v. 3.3.2 or higher installed (http://cran.r-project.org/) RStudio v. 1.0.136 or higher installed (https://www.rstudio.com/) MiKTeX, FULL version: Through the link below, download and extract ProTeXt, run Setup.exe and install MiKTeX full version: (http://ctan.mirror.garr.it/mirrors/CTAN/systems/windows/protext/) MiKteX, BASIC version (only if full version cannot be installed): Install basic version, open MiKTeX Package Manager, Repository, Synchronize. R packages: - (http://cran.r-project.org/): brew, data.table, dplyr, forecast, ggplot2, gtable, knitr, lazyeval, plyr, quadprog, readr, reshape, reshape2, RODBC, scales, sos, timeDate, tseries, XLConnect, xlsx, xtable, zoo, TTR. Basic readings: BROOKS C. (2002) "Introductory Econometrics for Finance", Cambridge University Press (Ch. 1-7) CHRISTOFFERSEN P.F. (2012) "Elements of Financial Risk Management", 2nd edition,Academic Press DIEBOLD F. Econometrics, available at http://www.ssc.upenn.edu/~fdiebold/Textbooks.html SINGH AK and DE ALLEN(2017) R in Finance and Economics. A Beginners Guide, World Scientific Publishing Paul Torfs & Claudia Brauer (2014) “A (very) short introduction to R“ https://cran.rproject.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf) The Econometrics of Financial Returns and Risk Measurement , available at www.igier.unibocconi.it/favero ) Detailed Syllabus 1. Introduction to R (Lectures 1-2) R overview and history, R-Studio environment (R scripts, Console, Workspace, History, Files, Plots, Packages, Help) Assigning values to variables, Help functions,Objects orientation in R (examples of objects, data types and object classes) Vectors and matrices (indexing vectors, recycling rule, sequences, passing arguments to functions, rep function), Not available data (NA), Dates, Logical operators, If else operator and the For loop The working directory, R packages (install and load packages), Import/export data from text files, The list object The apply functions, Benchmark code performances, User-defined functions, Environments in R, Read code from a file, Debugging, Plot functions,Probability distributions in R, Basic statistics functions Exercises and examples W. N. Venables, D. M. Smith and the R Core Team (2017) “An Introduction to R” (ftp://cran.rproject.org/pub/R/doc/manuals/r-devel/R-intro.pdf) John Verzani (2011) “Getting Started with RStudio” (https://www.cs.utexas.edu/~cannata/dataVis/Class%20Notes/Getting%20Started%20with%20RStudio.pdf) Guy Yollin (2011) “Introduction to R” (https://faculty.washington.edu/ezivot/econ424/RIntro.pdf) 2. Data Management (Lectures 3-4) Importing/exporting data, Data manipulation: basic commands (data preview, sorting, selecting, adding, dropping and reordering data Appending, subsetting and aggregating data, Merging and reshaping data,Advanced data format (data.table) Exercises and examples N. J. Horton, K. Kleinman (2015) “Using R and RStudio for Data Management, Statistical Analysis and Graphics - Second Edition” A. Jaynal, K. Kumar Das (2015) “Data Manipulation with R - Second Edition” 3. Probability and Statistics (Lectures 5-6) R for statistical analysis, Regression, seasonality, testing stationarity, ARIMA modelling Exercises and examples A. Coghlan (2017) “A Little Book of R For Time Series – Release 0.2” (https://media.readthedocs.org/pdf/alittle-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf) 4.Graphics and Reporting (Lectures 7-8) Basic Latex, Principles of Automated Reporting using knitr, Build a simple article in R, Build a simple beamer presentation in R, Embed plots and table in an article/beamer, Plotting with ggplot2 Exercises and examples A simple guide to LaTeX – Step by Step (https://www.latex-tutorial.com/tutorials/) Yihui Xie (2015) “Dynamic Documents with R and knitr – Second Edition” (https://yihui.name/knitr/) Hadley Wickham (2016) “ggplot2 Elegant Graphics for Data Analysis – Second Edition” 5.Econometric Modelling of Financial Returns an Introduction (Lectures 9-10) The Process of Econometric Modelling: model specification, estimation and simulation, model validations and applications The Data: Ken French database and Bob Shiller database An Historycal Perspective: The view from the 1960:Efficient Markets and the CER, Time-Series Implications, Returns at different horizons, The cross-section of returns, The volatility of returns, Implications for Asset Allocation Empirical Challenges to the traditional model: the DDG model and predictability of returns, Anomalies, the cross-section evidence, the behaviour of returns at high frequency, Implications of the new evidence, Predictive Models in Finance SLIDES, a ZIP file with an Rmd code for data exploration and the data Suggestions for further reading: The Motivation of the 2013 Nobel Prize in Economics Cochrane J.(1999) New Facts in Finance Forecasting Long-Run Returns: a View from the Market Prof. Shiller's webpage Prof.Fama's webpage 6. Modelling Returns: the CER and the CAPM (Lectures 11-12) The CAPM, the CAPM reduced form Model Estimation Model Simulation: Monte-Carlo and bootstrap an application Computing VaR by simulating CAPM and CER SLIDES, a ZIP file with an Rmd code for model estimation, simulation and VaR computation EXERCISE 7. Intepreting Regression Results (Lectures 13-14) Statistical significance and relevance Inference in the regression model The Partitioned Regression Model What If Analysis SLIDES,a ZIP file with an Rmd code on the interpretation of regression results EXERCISE 8. Model Mis-Specification(Lectures 15-16) Model Misspecification: under-parameterization, over-parameterization wrong assumptions on residuals' behaviour Testing the CAPM: time series and cross-sectional evidence (Fama McBeth) SLIDES, a ZIP file with an RMd code on testing the CAPM EXERCISE
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