Seminars

The practical content of the course is designed to be delivered in eight seminars. Each seminar consists of a notebook with the material that will be covered in the session and homework exercises that students are expected to solve on their own.

The course also includes a support notebook with discussion on more advanced R topics.

Seminar One

Classwork

Familiarize ourselves with R and RStudio
Learn some basic commands
Download and import financial data
Create a simple plot

Homework

Download the stock prices and returns from Amazon and Apple between January 1st, 1990 and December 31st, 2010
Load the data into R
Create two variables “amazon” and “apple” that hold the downloaded data of each stock
Do a simple plot for each of the stock’s returns

Seminar Two

Classwork

Install and load packages in R
Basic Data Handling
Save created Data Frames
Create, customize and export plots

Homework

Open the data from Amazon and Apple from the previous seminar
Use the dcast function to create RET and PRC data frames with the returns and prices
Make sure your returns are compounded and not simple
Save each data frame in your working directory
Plot the returns and prices for each stock, well-labeled, using a 2x2 grid
Export your plot

Seminar Three

Classwork

Learn how to work with statistical distributions
Explore random numbers and the Monte Carlo simulation
Visualize, analyse, and comment on the prices of a stock
Perfrom graphical analyses and statistical tests

Homework

Elaborate a commentary on the price of the Amazon stock. To do this:

Load the returns data frame you created in Seminar Two
Plot the returns for Amazon
Zoom into the dot-com years (2000-2002)
Find the best and worst perfoming days, and find out what happened on those dates
Compare the returns to a normal distribution graphically and using a test
Plot the ACF of the returns and returns squared
Use a 2x2 grid to elaborate four QQplots comparing the returns to a Student-T of 2, 3, 4 and 5 degrees of freedom

Seminar Four

Classwork

Build unvariate GARCH models
Plot GARCH outputs
Work with various specifications (ARCH, Student t, apARCH)
Relax the GARCH stationarity condition
Assess model quality using likelihood ratio tests and residual analysis
Learn about half-life and GARCH simulations

Homework

Choose a stock between Amazon and Apple and:

Fit a univariate GARCH(1,1) where conditional returns follow a normal distribution
Fit a univariate GARCH(1,1) where conditional returns follow a T-distribuion
Plot the estimated conditional volatilities against each other and comment
Fit an ARCH(1,1) model and perform a LR test versus the GARCH(1,1)

Seminar Five

Classwork

Introduce multivariate volatility models
Build a bivariate EWMA model
Run DCC models with different specifications
Compare models

Homework

Load the Amazon and Apple returns
Build a bivariate EWMA model for these stocks
Build a loop that creates an EWMA for values of lamba = 0.9, 0.91, 0.92,… 0.99
Create one plot for each stock with the estimated conditional volatilies for the different lambda
Fit a DCC tapARCH model and calculate the conditional correlation
Plot it and zoom into the dot-com bubble period (2000-2002)
Comment any findings

Seminar Six

Classwork

Solve exam-like questions regarding risk measures
Perform univariate and multivariate estimations of ES and VaR using Historical Simulation
Experiment with different estimation windows for Historical Simulation VaR
Build an EWMA VaR model

Homework

Load the Amazon and Apple returns
Perform a Historical Simulation VaR 99% forecast for each stock, use an estimation window of 1000 days
Perform a Historical Simulation VaR 95% forecast for each stock, use an estimation window of 1000 days
For each stock, plot the HS VaR forecast for both 95% and 99% in the same plot
Comment

Seminar Seven

Classwork

Implement GARCH VaR
Analyze and compare VaR forecasts between models
Perform backtests with violation ratios
Implement multivariate EWMA and HS VaR
Perform stress-testing

Homework

Use the six models covered (EWMA, HS300, HS1000, HS2000, GARCH300, GARCH2000) to backtest 1% VaR for both Amazon and Apple stocks individually.
Perform a stress-test in the dot-com years (2000-2002)

Seminar Eight

Classwork

Use the Black-Scholes equation to price an option
Simulate option prices with Monte Carlo
Experiment with different simulation sizes
Use analytical and simulation methods to get VaR
Calculate VaR for an option
Calculate VaR for a portfolio of stock and option

Homework

Repeat the seminar for a Call option instead of a Put