SYLLABUS  - STATISTICS - Master in Finance - USI- 2022-2023

Prerequisites

The course assumes prior knowledge of the following topics: probability, expectation, and variance of a random variable, principal continuous and discrete distributions of random variables, and basic notions of matrix algebra. Knowledge of the statistical software R is expected with the simultaneous Programming in Finance and Economics I course. 

Learning Objectives

The course aims to provide students with methodological and applied background on inferential statistics, multiple linear regression models, and specific procedures for analyzing financial time series.

Description

The course aims to introduce students to statistical inference: population and samples, likelihood function, point, and interval estimation, hypothesis testing, and prediction. The multivariate Gaussian distribution and multiple linear regression are described, along with diagnostics measures and predictions. Elements of time series data analysis are introduced: autocovariance and autocorrelation functions and related plots, white noise and stationary processes, and AR(1) and MA(1) processes. The course provides skills in using the semantics of the free software environment R for descriptive univariate and multivariate data analysis, inferential methods, and estimation of statistical models. Theory and practical applications are jointly developed to support students with deep theoretical and practical knowledge. The R environment within the RStudio and RMarkdown is employed to create live code, output, and comments on the results in the same interface and to produce reproducible documents.

Learning Methods

Lectures ex-cathedra
Students are requested to bring their laptops.

Compliant with COVID-19 guidelines.

Exam 

Final written exam with open questions on the theoretical part and an application to develop using the R software.

Readings

Teaching notes will be distributed during the course. Some suggested books are the following:

Agresti, A., & Kateri, M. (2021). Foundations of Statistics for Data Scientists: With R and Python. Chapman and Hall/CRC.

Casella, G., & Berger, R. L. (2002). Statistical inference (2nd Edition). Cengage Learning.

Gentle, J. E. (2020). Statistical Analysis of Financial Data: With Examples in R. Chapman and Hall/CRC.

R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Yihui X. (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC.