No prerequisites, but a base knowledge of statistics is preferable
The course introduces the main statistical, mathematical, and computational tools used for the analysis of business and financial data. We start from an introduction about the data collection process and with the evaluation of data quality. Then, we focus on the main analytical tools for dealing with cross-sectional data (inference, one-way ANOVA, Multiple regression analysis). In the follow part we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques using commonly used software (SAS and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.
In particular, in the second module we discuss the characteristics of financial instruments listed in the markets (stocks, bonds, and financial derivatives), then we present common statistical tools used to model financial time series, namely random walk models and ARMA, ARCH and GARCH processes. The models are then studied for the measurement of financial risk introducing measures such as VaR and CVaR.
The students will be able to use time-series models to study the evolution of stock market prices as well as their volatility, and they will learn how to compute risk measures and assess their statistical significance. They will also be able to implement the models using state-of-the-art software, validate the results, and discuss their statistical and economic interpretation.
- Introduction to financial markets: stocks, bonds and other financial instruments.
- Time series analysis: univariate and multivariate random variables, random walk processes, efficient market hypothesis.
- Random processes for stock prices: ARMA processes for non-stationary time series, GARCH processes for heteroskedastic time series, statistical tests for stationarity and heteroskedasticity
- Risk measures: conditional and unconditional Value at Risk (VaR) and Conditional Value at Risk (CVaR), risk backtesting.
The course consists of traditional lectures and laboratory sessions where the introduced techniques are practically implemented and interpreted using a software.
The exam is made by two parts: one for each module. Each exam awards up to 30 points.
The students have to reach a positive (i.e., at least 18) score for both modules. The final full course evaluation is computed as simple average of the two scores.
For both modules, the exam is written and includes an evaluation about the ability acquired by the student in working with the introduced software. There are no differences for attending and for non-attending students.
The module 87173-MOD2 (3 CFU) is part of the 6 CFU course.
If the course will be on line or partially on line, changes can be made compared to what is stated in the syllabus to the course contents and to the exam organization.