ADVANCED TOPICS IN FINANCE AND INSURANCE II
Syllabus
EN
IT
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.
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.
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). Knowledge of the learning program ARPM and the corresponding Lab.
Program
The course is based on the ARPM Marathon, available through the ARPM interactive learning platform, and it covers the last two learning modules:
1) Quantitative Risk Management covers portfolio risk/liquidity adjusted valuation, return distribution and risk statistics; and their decomposition into contributions from key risk factors:
- Aggregation, which is the computation of a portfolio’s value, including enterprise portfolios, and its ex-ante return distribution in both regular and stress markets
- Ex-ante evaluation, which is the computation of risk measures such as utility-based measures, VaR, CVaR, and more general spectral measures
- Ex-ante attribution, which is the decomposition of the ex-ante return and risk measures into key risk factors
2) Quantitative Portfolio Management covers static and dynamic portfolio construction, and optimal execution:
- Optimization: convex programming, quadratic regularization, selection problems
- One-period portfolio construction: total return and relative value mean-variance, fundamental law of active management, tails and non-normality
- Dynamic portfolio construction: cross-sectional strategies, time series strategies
- Execution: market impact modeling, order scheduling, order placement
The previous two modules are covered in the course "Advanced Topics in Finance and Insurance I".
1) Quantitative Risk Management covers portfolio risk/liquidity adjusted valuation, return distribution and risk statistics; and their decomposition into contributions from key risk factors:
- Aggregation, which is the computation of a portfolio’s value, including enterprise portfolios, and its ex-ante return distribution in both regular and stress markets
- Ex-ante evaluation, which is the computation of risk measures such as utility-based measures, VaR, CVaR, and more general spectral measures
- Ex-ante attribution, which is the decomposition of the ex-ante return and risk measures into key risk factors
2) Quantitative Portfolio Management covers static and dynamic portfolio construction, and optimal execution:
- Optimization: convex programming, quadratic regularization, selection problems
- One-period portfolio construction: total return and relative value mean-variance, fundamental law of active management, tails and non-normality
- Dynamic portfolio construction: cross-sectional strategies, time series strategies
- Execution: market impact modeling, order scheduling, order placement
The previous two modules are covered in the course "Advanced Topics in Finance and Insurance I".
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.
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 the 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 the examination test is expressed in thiertieths.