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 following 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 by means of some of the most common software (SAS and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.
• Data collection: survey methods, sample selection, questionnaire design.
• Data quality: how to detect and treat data issues.
• Base inference: main statistical distributions and base of hypothesis testing, mean and proportion testing, testing group differences.
• One-way ANOVA for comparing group means and for experimental studies.
• Multiple regression analysis: setting and running the analysis, interpreting the output; introduction of logistic regression.
• 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 course 87173 (6 university credits) is made by two Modules (each one is worth 3 university credits).
"In case of provisions of the competent authority on containment and management of epidemiological emergency, the teaching may be subject to changes from what is declared in the syllabus in order to make the course and examinations in line with the regulations."