There are no formal prerequisite, however, to ensure that students can achieve the educational objectives of the course, it is advisable to participate in the "crash courses" delivered at the beginning of the academic year.
The course aims to strengthen the knowledge of probability and statistics obtained during the three-year degree course and to develop abilities useful for applying statistical methods in the economic and financial fields. At the end of the course, the student will gain the ability to:
a) choose, apply and test appropriate statistical methods and models for the analysis of different types of economic and financial data;
b) use the free open-source statistical software R (http://www.r-project.org) for statistical data analysis, as well as for modeling and forecasting economic and financial time series;
c) interpret the results in a decision making perspective.
For 9 CFU students from the EF degree:
- Financial variables: returns and distributional properties of returns.
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for economic and financial data analysis.
- Statistical methods for exploratory data analysis and univariate distribution modeling (histogram, QQ-plot and normal probability plot, data transformation, distribution parameters, skewness and kurtosis indexes, tests of normality, heavy tails distributions).
- Multivariate statistical models (multivariate Normal and T distribution, covariance matrix, linear combinations of random variables).
- Multiple linear regression: basics and troubleshooting (model estimation, ANOVA, model evaluation and selection, check of model assumptions).
- Stochastic processes and models for time series (AR, MA, ARMA and ARIMA models): definition, estimation and forecasting.
For 6 CFU students from the EDA program (part 1+part3):
- Financial variables: returns and distributional properties of returns.
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for economic and financial data analysis.
- Statistical methods for exploratory data analysis and univariate distribution modeling (histogram, QQ-plot and normal probability plot, data transformation, distribution parameters, skewness and kurtosis indexes, tests of normality, heavy tails distributions).
- Stochastic processes and models for time series (AR, MA, ARMA and ARIMA models): definition, estimation and forecasting.
The course consists in class lectures and R lab sessions. The lectures & labs calendar will be published at the beginning of the course on the Moodle e-learning platform. Labs will take place within the hours scheduled for the course (2 hours per week, approximately).
The procedure and content of the exam will be the same for both attending and non-attending students.
The exam consists in:
- a test including open-ended and T/F questions (concerning theoretical topics or short applications of the studied methods);
- exercises to be solved with the use of the R software (in order to evaluate the ability of the student in analysing financial data and in the interpretation of statistical outputs).
The two parts of the exam (theoretical and practical) are each worth 50% of the total score, approximately.
The official course book is:
Ruppert D., Matteson, D.S. (2015). Statistics and Data Analysis for Financial Engineering with R examples (second edition). Springer.
http://www.springer.com/us/book/9781493926138#aboutBook