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, exploratory analyses, statistical tests and regression analysis). In the follow part we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARIMA, seasonal ARIMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques using commonly used software (SAS Studio 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,ARIMA, seasonal ARIMA, ARCH and GARCH processes. The models are then studied for forecasting (point forecast and prediction intervals), and for the measurement of financial risk with the introduction of the Value at Risk (VaR). 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.
