PROBABILITY AND STATISTICS FOR BUSINESS AND FINANCE (ADVANCED)

Attività formativa monodisciplinare
Codice dell'attività formativa: 
910001-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2019/2020
Insegnamento (nome in italiano): 
PROBABILITY AND STATISTICS FOR BUSINESS AND FINANCE (ADVANCED)
Insegnamento (nome in inglese): 
Probability and Statistics for Business and Finance (advanced)
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
STATISTICA (SECS-S/01)
Anno di corso: 
1
Anno accademico di offerta: 
2019/2020
Crediti: 
9
Responsabile della didattica: 
Altri docenti: 
Mutuazioni

Altre informazioni sull'insegnamento

Lingua: 
Inglese
Ciclo: 
Primo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
72
Ore di studio individuale: 
153
Ambito: 
Statistico-matematico
Materiali didattici: 
Prerequisites

Good knowledge of the topics taught in the three-year bachelor degree program of Statistics.

Educational goals

The course aims at providing the knowledge of 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, modeling and forecasting of financial time series;
c) interpret the results in a decision making perspective.

Course content

For 9 CFU students:
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for financial data analysis.
- Financial variables (i.e. returns and distributional properties of returns).
- Statistical methods for exploratory data analysis and univariate distribution modeling (histogram, quantiles, QQ-plot, data transformation, location, scale and shape 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 (estimation, ANOVA, model selection, check of model assumptions).
- Stochastic processes and models for time series (MA, AR, ARMA and ARIMA models): definition, estimation and forecasting.
- GARCH models for high volatility data: definition, estimation and forecasting.

For 6 CFU students:
- Review of the main statistical concepts (e.g. random variables, sampling distributions, hypothesis testing) necessary for financial data analysis.
- Financial variables (i.e. returns and distributional properties of returns).
- Statistical methods for exploratory data analysis and univariate distribution modeling (histogram, quantiles, QQ-plot, data transformation, location, scale and shape 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 (estimation, ANOVA, model selection, check of model assumptions).
- Essentials of stochastic processes and models for time series: definition, estimation and forecasting.

Textbooks and reading lists

The official course book is:
Ruppert D., Matteson, D.S. (2015). Statistics and Data Analysis for Financial Engineering with R examples (second edition). Springer.
More information about the official course book at the following links:
- http://www.springer.com/us/book/9781493926138#aboutBook
- https://people.orie.cornell.edu/davidr/SDAFE2/index.html

Suggested additional book:
- Carmona R. (2014). Statistical analysis of financial data in R (second edition). Springer.

About R software, documentation is freely available at the following link: https://www.r-project.org/other-docs.html.

Teaching methods

The course consists in class lectures and lab sessions. The lecture & lab calendar will be published before the beginning of the course on the e-learning platform; labs will take place within the hours scheduled for the course (2 hours per week, approximately).

Assessment and Evaluation

The exam consists in:
- a written test including open-ended and test questions (concerning theoretical topics or short applications of the studied methods);
- exercises to be solved with the use of R software (in order to evaluate the ability of the student in analysing financial data and in the interpretation of software output).
The theoretical and practical sections are each worth 50% of the total score, approximately.

In November 2019 and January 2020 there will be two partial exams, each on a partial part of the program. The final grade will be computed as weighted average of the intermediate scores. Dates and topics of the intermediate exams will be provided during the course.

Further information

Attending class lectures and R labs is strongly recommended.