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30188,30285 EMPIRICAL METHODS FOR FINANCE (INTRODUCTION TO ECONOMETRICS FOR FINANCE)


30285 - EMPIRICAL METHODS FOR FINANCE

Carlo A. Favero- Christian Skov Jensen

 

Course Objectives: Active Learning

The objective of this course is to introduce the main econometric methods and techniques used in empirical finance.  This is an ambitious task that will bring together different type of knowledge: finance theory, statistics, programming.
You will learn how to use software, in particular the R software, to specify, estimate and simulate model of financial data to be used for asset allocation, risk measurement and risk management.
The teaching strategy is based on providing inputs to students that are supposed to active elaborate them to produce their knowledge. The choice of inputs and the mapping of inputs into knowledge is the students’ responsibility. The course is designed to give opportunities. The decision of how many opportunities to take and how to take them is left to course participants. The final assessment will be designed to evaluate the solidity of the foundation in the relevant tools for financial time-series modelling achieved by the students at the end of the course. 

The Inputs
The main inputs provided to the students will be references, slides, notes, draft R codes and exercises designed to provide challenges that will stimulate learning. The empirical applications will be based on databases freely available on the web. Students are expected to download and install the R and Rstudio packages on their PC at the beginning of the course.

Assessment Methods

The level of knowledge reached by the end of the course will be assessed in a final computer based exam: In the final exam students will be required to use their knowledge to tackle empirically some variants of the empirical issues addressed during the lectures and the exercises. During the exam students will be required to modify the R codes that they have built during the course to generate answers to the questions posed in the exercises. The exam wiil be open book and it is  highly recommended to prepare it via gradual learning during the course using all inputs made available. Final marks will be obtained by curving the raw marks using Bocconi's distribution.   

Prerequisites
Students are expected to have attended a core course in statistics and to be familiar with undergraduate calculus and linear algebra. Prior exposure to financial courses (financial markets and institutions, investments and corporate finance) is required to understand the applications covered in class and it is taken for granted.

Teaching Assistant
Federico Mainardi (federico.mainardi@studbocconi.it) 
OFFICE HOURS: wednesdays 11-13 (Room 2-c3-01)

MOCK EXAM DECEMBER 2017EXAM SEPT 2018, R CODE and DATASOLUTION SEPT 2018
MOCK EXAM DECEMBER 2018R CODE 

EXAM 19 DECEMBER 2018 , TEXT,  SOLUTION 
EXAM 23 JANUARY 2019R CODE,DATASOLUTION  

EXAM 4 JULY 2019, SOLUTION  

EXAM 28 AUGUST 2019, SOLUTION 

Course Content Summary

Lecture 1: Where are we going?
The Econometrics of Financial Returns
The dimensions of the data
What makes Econometrics interesting
Econometric Modelling of Financial Returns
Building, Estimating, Validating and Using an Empirical Model
The Data
INPUTS
SLIDES
Notes on the Econometrics of Asset Allocation and risk Measurement, Ch.1

Lecture 2: Where do we stand ?
Entry Test on basic knowledge in finance, statistics, probability
RESULTS of the Entry Test 
SOLUTIONan R code for Question 1

INPUTS 
AN INTRODUCTION TO MATRIX ALGEBRA, STATISTICS AND PROBABILITY (COURTESY OF "THE MASTER", PROF. F. CORIELLI)
A SIMPLE DERIVATION OF THE CAPM  by C. Deeley 

 

Lecture 3-4: An introduction to R

Before the lecture:
Install R and R studio on your computer and learn how to run them
Learn what is a package and how to install it
Understand what is a view
INPUTS:
Singh AK and DE Allen(2017) R in Finance and Economics. A Beginners Guide, World Scientific Publishing, Ch 1
Torfs Brauer "A Very Short Intro to R" 
An introduction to R Markdown

Intro R Code (from Singh and Allen) 
all R codes used in Singh and Allen are downloaded at 
http://www.rforresearch.com/r-in-finance-economics

Topics of the lecture
Data Objects in R (data types) and Data Structures In R (Vectors, Matrices, Arrays, Data Frames, Lists)
Data Handling in R (Importing and Exporting data)
Programming and Control Flow (if-else statements, using switch, loops, functions in R)
Dynamic Documents with R Markdown: build a report with all results and comments

INPUTS
Singh AK and DE Allen(2017) R in Finance and Economics. A Beginners Guide, World Scientific Publishing, Ch 2,3,4
Heiss F. (2016)  Using R for introductory Econometrics http://urfie.net/read/mobile/index.html#p=4, Section 1: Introduction
Yihui Xie, Dynamic Documents with R and Knitr,  Chapman and Hall 

R CODES (from Singh and Allen) : Data ObjectsData HandlingProgramming
An Illustration of R coding: the analysis of performance in the Entry Test  
DATAR CODER Markdown CODE

EXERCISE 1 Write an R code that answers to all the ToDo points  in  Torfs P. and C. Bauer(2014) “A (very short) introduction to R” SOLUTION

Lecture 5:  Returns
Simple and log Returns
Multi-period returns and annualized returns
Working with Returns
Stock and Bond Returns
Stock Returns and the dynamic dividend growth model
Bond Returns: Yields-to-Maturity, Duration and Holding Period Returns
Graphical Analysis of the Data
Matrix Representation of the data
INPUTS
SLIDES
Notes on the Econometrics of Asset Allocation and risk Measurement, Ch.2

Lecture 6-7:  Data Analysis with R
The Fama French and Shiller databases
Using R to load, transform and analyze time-series data
INPUTS
the datasets
a Rmd code for the analysis of Shiller database,
a Rmd code for the analysis of the Fama-French database
Portfolio optimization with R:fPortfolioPortfolioAnalytics 
Wurtz D., Y. Calabi, W.Chen and A.Ellis(2009) "Portfolio Optimization with R/Rmetrics, Rmetrics Association & Finance Online, Zurich 
Zivot A. and J.Wang (2006) "Modelling Time-Series with S-Plus", Springer 

EXERCISE 2: Learn to import, transform and graph data by answering to these questionsSOLUTION

Lecture 8: Modeling and Simulating Returns with R 
Assessing Models by Simulation: Monte-Carlo and Bootstrap Methods
Stocks for the long run
INPUTS
SLIDES
a Rmd code

Lecture 9: Estimating Linear Models of Returns
1      Econometric Modelling of Financial Returns: a general framework  
2   The Reduction Process
3   Exogeneity and Identification  
4   Estimation Problem: Ordinary Least Squares
4.1   Derivation of the OLS estimates
4.2   Properties of the OLS estimates
4.3  Hypotheses
4.4  Unbiasedness, Variance and Gauss-Markov theorem
4.5   Residual Analysis
4.6 The R-squared
INPUTS 
SLIDES 
Notes on the Econometrics of Asset Allocation and risk Measurement, Ch.3

Lecture 10: CAPM estimation and simulation with R 
estimation, simulation and VaR with a more articulated model
INPUTS
EXERCISE 3SOLUTION
an R code for asset allocation with a single factor model 

Lecture 11-12: Interpreting Regression Results
5.1   Statistical significance, Relevance and Mis-specification
5.2   Inference in the Linear Regression Model
5.2.1   How to formalize the relevant hypothesis
5.2.2   How to build the Statistics
5.3   The partitioned regression model
5.4   Testing Restrictions on a subset of coefficients 
5.5  Relevance of a Regression
5.5.1 The R2 as a measure of relevance 
5.5.2 The Partial Regression Theorem
5.5.3 the Partial R2

INPUTS 
SLIDEShypotheses testing and interpreting results in R,  partitioned regression in R

Lecture 13:
EXERCISE 4: on Estimation and Intepreting Regression Results
INPUTS:
The text of the exercise,

Lecture 14-16:Model Mis-Specification
Mis-specification in the choice of variables
Under-parameterization
Over-parameterization
Mis-specification in  omitting parameters constraints
Estimation under linear constraints
Misspecification of  residuals' behaviour
INPUTS
SLIDES 
EXERCISE 5: Model Mis-Specification
a draft Rmd code

Lecture 17 : Testing the CAPM
Fama MacBeth and the cross section of returns
Multi-Factor Models  
INPUTS
SLIDES
EXERCISE 6a draft Rmd code

 

Lecture 18: An Historycal Perspective
1. The view from the 1960:Efficient Markets and the CER
1.1 Time-Series Implications
1.2 Returns at different horizons
1.3 The cross-section of returns
1.4 The volatility of returns
1.5 Implications for Asset Allocation
2. Empirical Challenges to the traditional model
2.1 the DDG model and predictability of returns
2.2 Anomalies
2.3 the cross-section evidence
2.4 the behaviour of returns at high frequency
3. Implications of the new evidence
4. Predictive Models in Finance
INPUTS
SLIDES

Lecture 19-21: Univariate Time-Series
Analysing Time-Series: Fundamentals
Conditional and Unconditional Densities
Stationarity
ARMA Processes
Persistence: A Monte Carlo Experiment
Estimation of ARMA models. The Maximum Likelihood Method
Putting ARMA models at work
INPUTS
SLIDESR Code to replicate SLIDES EXERCISEDraft Rmd code 
A R code for trading strategy with predictabilitydata 

Lectures 22-24. Modelling Heteroscedasticity

A look at the data: Correlation and non-normality
GARCH Modelling
Representation
Testing for GARCH 
Maximum Likelihood Estimation
Forecasting
Beyond GARCH: threshold models
Simulation
VaR with GARCH
Backtesting VaR
INPUTS
SLIDESR Code to replicate slides  EXERCISEDraft Rmd code

References
The Econometrics of Financial Returns and Risk Measurement , available at www.igier.unibocconi.it/favero 
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
HEISS F. (2016)  Using R for introductory Econometrics. http://www.urfie.net

 

Last update 28/08/2019



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