Quantitative Methods in Finance

The aim of this course is to deepen the knowledge of inferential methods for empirical research with applications focusing on finance but also including economics, management and marketing, both at a univariate and multivariate level. The focus of the course will be mainly applied. Together with the theoretical concepts, data sets derived from empirical research, experimental data and questionnaires will be analyzed.
The free-ware statistical software "R" (free version of the commercial software "Splus", one of the main software used for statistical purposes) will be introduced. The software can be downloaded from the website: http://www.r-project.org/.
Data, imported from an Excel spreadsheet into "R", will be analysed. In particular the statistical instruments needed to compare two or more samples and to detect and analyse possible dependence links between two or more characters (both qualitative and quantitative) will be provided.

Prerequisites
The students will be assumed to have learned, in previous classes, the following concepts of probability theory and descriptive statistics.
Students in need to refresh their probability background can be advised to sit in the course Introduction to Statistics
On the website of the course there are also lecture notes to review the topics mentioned below that are a pre-requisite for the course.

Probability Theory:
Introduction to probability:
definitions, concept of marginal and joint probability, low of total probability, conditional probability, notion of independence
Random variables:
discrete (Bernoulli, Binomial, Geometric, Poisson, Uniform) continuous (Uniform, Gaussian or Normal, Exponential, Student-T, Chi-square)

Descriptive Statistics:
Univariate:
measure of location (mean, median, mode) and dispersion (variance, std deviation, quantiles)
Bivariate:
two way tables, joint and marginal distributions, covariance and correlation
Graphical instruments to visualize data

Details of the course
The course focuses on inferential statistics both theoretical and applied (with a focus on financial applications).

Main topics:
- Theory of point estimation (methods and properties of estimators)
- Construction of confidence intervals
- Theory of hypothesis tests

References
Lecture notes will be available on the e-learning website

Textbooks
P. Newbold, W. Carlson, B. Thorne. Statistics for Business and Economics, Prentince Hall, 2010. 7th Edition, (also available in Italian).
M.J. Crawley. Statistics: An Introduction using R, Wiley, New York, 2005.