Ateneo - UPO

The course introduces the analysis of financial data and simulation techniques in the financial framework by using some ad-hoc softwares.

Lecture materials

References:
- R. Carmona (2014). Statistical Analysis of Financial Data in R. Springer.
- S.T.Rachev, M. Hochestotter, F.J.Fabozzi, S.M. Focardi (2010) Probability and Statistics for Finance. Wiley.

The aim of the course is the knowledge and the mastery of inferential methods and main forecasting techniques.
Moreover, the course aims to provide the ability to apply these techniques autonomously in a financial context and to present the results effectively.

Linear algebra, calculus, elements of probability and statistics.

Lectures, exercises, computer lab lessons.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The evaluation is based on a compulsory written and oral exam including:
- theoretical questions whose scope is to verify the knowledge level,
- exercises to verify the abilities in applying the introduced numerical tools,
- structured problems to test abilities in providing coherent comments on the results and communicating them with a proper statistical language.

Please note that after the third attempt to sit the exam in the course of a calendar year, the registration block is automatically applied.

1. Elements of probability. Stochastic independence. Random variables and distributions: mass and density functions. Generating functions. Bivariate probability densities. Random Vectors. Gaussian random vectors and their properties.
2. Statistical Inference. Plug-in approach and Empirical estimates. Maximum likelihood method. Confidence interval. Parametric and nonparametric bootstrap approaches.
3. Testing hypothesis. Errors of I and II type. Critical region. Power of a test. P-value. Parametric tests. Goodness-of-fit tests and normality test (Kolmogorov-Smirmov, Jarque-Bera).
4. Multiple linear regression. OLS principle and maximum likelihhod estimate in the Gaussian case. Test on coefficients and test F.
5. Intoduction to stochastic processes. Weak stationarity. Brownian motion. Simulation of a random process.