Good knowledge of the fundamentals of Statistics (i.e. descriptive statistics, probability, inferential statistics, linear regression model).
The course aims at introducing the main statistical methods for the quantitative analysis of financial data. 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 financial data;
b) use the free open-source statistical software R (http://www.r-project.org) for the statistical analysis, as well as for modeling and forecasting financial time series;
c) interpret the results in a decision making perspective.
For 9 CFU students:
- Financial variables: returns and distributional properties of returns.
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for 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.
- GARCH models for high volatility data: definition, estimation and forecasting.
For 6 CFU students:
- Financial variables: returns and distributional properties of returns.
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for 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).
- Essentials of stochastic processes and models for time series: 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 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.A positive evaluation of the theoretical part is required in order to pass the exam.
In November 2020 and January 2021 there will be two partial exams, each on a part of the program. The final grade will be computed as sum of the two intermediate scores. Dates and topics of the intermediate exams will be announced during the course.
- Attending class lectures and R labs is strongly recommended.
- If the course will be delivered online (totally or partially), changes may occur in the program and/or in the exam.
- More information about the official course book are available at the following links:
http://www.springer.com/us/book/9781493926138#aboutBook
https://people.orie.cornell.edu/davidr/SDAFE2/index.html
- Documentation about R software is freely available at the following link: https://www.r-project.org/other-docs.html.