ADVANCED TOPICS IN FINANCE AND INSURANCE
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES: Learning of the main mathematical/statistical techniques used in the modeling and analysis of financial markets and in the measurement and management of risk.
KNOWLEDGE AND UNDERSTANDING: Students acquire knowledge of the main mathematical and statistical methods used for the analysis of financial markets. Alongside the more purely modeling aspects, application aspects are introduced through the use of dedicated software in multiple case studies.
APPLYING KNOWLEDGE AND UNDERSTANDING: At the end of the learning path the students are able to apply the acquired knowledge and techniques for the analysis of numerous financial products and risk measurement and management, also through the implementation of the presented techniques by means of programming languages.
MAKING JUDGEMENTS: The course aims to provide a broad and coherent view of the various aspects concerning risk analysis and management that can guide decisions and problem solving in financial contexts characterized by information that is often limited and rapidly evolving.
COMMUNICATION SKILLS: The student must be in possession of adequate knowledge that allows him to communicate clearly, to specialist and non-specialist interlocutors, the theoretical context of reference, and summarize the empirical evidence concerning the decisional problem raised in the financial framework.
LEARNING SKILLS: The student must be able to deal with the problems of analyzing complex financial products, risk measurement and management, and the necessary updating of knowledge and models in continuous evolution in the financial market in a largely autonomous way.
KNOWLEDGE AND UNDERSTANDING: Students acquire knowledge of the main mathematical and statistical methods used for the analysis of financial markets. Alongside the more purely modeling aspects, application aspects are introduced through the use of dedicated software in multiple case studies.
APPLYING KNOWLEDGE AND UNDERSTANDING: At the end of the learning path the students are able to apply the acquired knowledge and techniques for the analysis of numerous financial products and risk measurement and management, also through the implementation of the presented techniques by means of programming languages.
MAKING JUDGEMENTS: The course aims to provide a broad and coherent view of the various aspects concerning risk analysis and management that can guide decisions and problem solving in financial contexts characterized by information that is often limited and rapidly evolving.
COMMUNICATION SKILLS: The student must be in possession of adequate knowledge that allows him to communicate clearly, to specialist and non-specialist interlocutors, the theoretical context of reference, and summarize the empirical evidence concerning the decisional problem raised in the financial framework.
LEARNING SKILLS: The student must be able to deal with the problems of analyzing complex financial products, risk measurement and management, and the necessary updating of knowledge and models in continuous evolution in the financial market in a largely autonomous way.
Prerequisites
Basic knowledge of general mathematics (matrices and vectors, series, limits, continuity,
derivatives, integrals), probability (random variables, distribution and density functions,
expected values) and of the main financial products (shares, bonds and derivatives).
derivatives, integrals), probability (random variables, distribution and density functions,
expected values) and of the main financial products (shares, bonds and derivatives).
Program
The course is based on the ARPM Quant Marathon. The main topics cover
I) Financial Engineering for Investment. This module covers valuation across instruments
and asset classes:
- Valuation across financial instruments, including linear pricing theory foundations,
risk-neutral valuation for derivatives, capital asset pricing framework.
- Identification, modeling and forecasting of key risk drivers for the returns of equities, fixed
income, derivatives, credit, high frequency, foreign exchange
- Repricing techniques: Monte Carlo full repricing, analytical Greeks approximations.
II) Data Science for Finance. This module covers the statistical tools needed to model and
estimate the joint dynamics of the markets:
- Multivariate distributions and notable classes: elliptical, exponential, discrete
- The “mean-covariance/linear” ecosystem: mean vector, covariance matrix, ellipsoid, affine
equivariance, correlation, linear prediction, whitening
- Estimation of the “mean-covariance/linear” ecosystem: historical, maximum likelihood,
Bayesian, random matrix theory and shrinkage
- Linear factor models: regression, principal component analysis, factor analysis,
cross-sectional models
- Machine learning models; Feature engineering and enhancements: feature bases, trees,
neural networks, gradient boosting, lasso/ridge regularization, random forests, etc.
I) Financial Engineering for Investment. This module covers valuation across instruments
and asset classes:
- Valuation across financial instruments, including linear pricing theory foundations,
risk-neutral valuation for derivatives, capital asset pricing framework.
- Identification, modeling and forecasting of key risk drivers for the returns of equities, fixed
income, derivatives, credit, high frequency, foreign exchange
- Repricing techniques: Monte Carlo full repricing, analytical Greeks approximations.
II) Data Science for Finance. This module covers the statistical tools needed to model and
estimate the joint dynamics of the markets:
- Multivariate distributions and notable classes: elliptical, exponential, discrete
- The “mean-covariance/linear” ecosystem: mean vector, covariance matrix, ellipsoid, affine
equivariance, correlation, linear prediction, whitening
- Estimation of the “mean-covariance/linear” ecosystem: historical, maximum likelihood,
Bayesian, random matrix theory and shrinkage
- Linear factor models: regression, principal component analysis, factor analysis,
cross-sectional models
- Machine learning models; Feature engineering and enhancements: feature bases, trees,
neural networks, gradient boosting, lasso/ridge regularization, random forests, etc.
Books
On-line ARPM Lab platform
Bibliography
A. Meucci, Risk and Asset Allocation, Springer 2009
Teaching methods
The course is attended online by students through the ARPM platform according to the
program indicated weekly by the teachers. Furthermore, every week there is a meeting in
the classroom with the teachers on the program (flipped classroom) and a set of exercises
are proposed that the students must solve and submit in electronic format.
program indicated weekly by the teachers. Furthermore, every week there is a meeting in
the classroom with the teachers on the program (flipped classroom) and a set of exercises
are proposed that the students must solve and submit in electronic format.
Exam Rules
The learning assessment is based on three criteria:
1) attendance to the weekly flipped classrooms. Students divided into small groups are
offered themes/problems to be addressed/solved within a given time to discuss with the rest
of the class.
2) Delivery of homeworks. Every week there are problems that students must solve and
submit in electronic format.
3) Final written test. The final exam consists of open-ended, "open book" questions on each
of the modules into which the program is divided, "Financial Engineering for Investment" and
"Data Science for Finance." The questions include theoretical/modeling questions and
problem-solving/examples addressed during the course. The student is expected to
understand the main mathematical/statistical techniques used in financial market modeling
and analysis and their subsequent independent application to complex financial products for
risk measurement and management. In addition, communication skills in terms of language
properties and clarity of exposition are assessed in adherence with the Dublin descriptors.
The score of th examination test is expressed in thiertieths.
1) attendance to the weekly flipped classrooms. Students divided into small groups are
offered themes/problems to be addressed/solved within a given time to discuss with the rest
of the class.
2) Delivery of homeworks. Every week there are problems that students must solve and
submit in electronic format.
3) Final written test. The final exam consists of open-ended, "open book" questions on each
of the modules into which the program is divided, "Financial Engineering for Investment" and
"Data Science for Finance." The questions include theoretical/modeling questions and
problem-solving/examples addressed during the course. The student is expected to
understand the main mathematical/statistical techniques used in financial market modeling
and analysis and their subsequent independent application to complex financial products for
risk measurement and management. In addition, communication skills in terms of language
properties and clarity of exposition are assessed in adherence with the Dublin descriptors.
The score of th examination test is expressed in thiertieths.