Finance and insurance

Educational objectives

The course aims to provide students with practical skills in the use of key statistical methods and models for the analysis of univariate time series, with a focus on financial phenomena. Students will acquire the ability to use statistical software to build interpretative and predictive models and will be able to identify and apply the most appropriate models for studying and forecasting financial market data, including considering their volatility. The practical use of tools such as R will foster a thorough understanding of the functioning of theoretical models and foster their informed application in real-world contexts. Upon completion of the course, students will have developed a solid understanding of empirical data analysis and practical skills useful for completing their final dissertation.

Educational objectives

The course aims to provide students with practical skills in using the Python programming language to tackle concrete problems in finance, including market data analysis, risk management, and the implementation of computational algorithms. Lectures will cover Python syntax, the main libraries for data manipulation, retrieval and visualization of financial data, as well as scientific computing tools and some elements of machine learning applied to finance.
The laboratory approach, alternating between instructor explanations and practical coding activities, is designed to develop operational autonomy, analytical skills, and critical awareness in the use of computational tools. By the end of the course, students will be able to design and implement computational solutions for financial problems, understand and explain the operation of Python code and the algorithms implemented. They will acquire skills in identifying the most appropriate techniques, using Python libraries, and critically interpreting results. The training provided will enhance the ability to make independent judgments on methodological choices, communicate results clearly both orally and in writing, and consolidate an autonomous learning method useful for further computational and financial studies. The skills acquired complement the theoretical training, preparing students for professional engagement in finance or for pursuing advanced study programs.

Educational objectives

The educational regulations 270 provide, within each Degree Program, a specific number of credits to be allocated to "student's elective activities." The number of credits provided for this course is 9. These activities consist of exams related to modules offered in the Master's Degree programs of the Faculty or other Faculties at Sapienza.

Educational objectives

The final thesis of the Master’s degree consists of the preparation and discussion of a dissertation, which demonstrates the student’s acquisition of advanced knowledge and specialized skills in quantitative models, computational techniques, and their application to financial, insurance, and risk management problems. The thesis represents an in-depth methodological and experimental experience, in which the student integrates knowledge acquired across the program, engages with scientific literature, and produces an original contribution on the chosen topic. Students will develop the ability to analyze complex phenomena, select and apply the most appropriate quantitative model, collect and process data, and interpret market and risk scenarios using digital tools such as Excel, dashboards, simulators, and dedicated software. The thesis work also fosters collaborative skills through interactions with faculty and peers and strengthens the ability to communicate analysis results clearly, concisely, and technically, with particular attention to presenting and arguing investment, hedging, and risk management strategies. At the end of the program, students will be able to independently update their quantitative and regulatory skills according to market developments and evolving risk management techniques. The thesis certifies scientific maturity and critical autonomy necessary to pursue advanced studies, such as second-level Master’s programs and PhDs, and provides solid preparation for the actuarial professional exam or professional entry into complex financial and insurance contexts.

Educational objectives

The educational regulations 270 provide, within each Degree Program, a specific number of credits to be allocated to "student's elective activities." The number of credits provided for this course is 9. These activities consist of exams related to modules offered in the Master's Degree programs of the Faculty or other Faculties at Sapienza.

Educational objectives

The course provides students with computational tools based on the Python programming language to tackle practical problems in actuarial and insurance contexts. During the laboratory sessions, students will explore Python syntax and the main libraries for retrieving, manipulating, and visualizing actuarial data, as well as scientific computing libraries and machine learning tools applied to actuarial problems. The teaching approach is practice-oriented: instructor explanations alternate with direct coding implementation by the students, who apply the knowledge acquired to solve real problems. In this way, students develop autonomy in selecting the most appropriate computational techniques, communication skills for presenting results, and the ability to independently learn new tools and methodologies. By the end of the course, students will be able to approach actuarial problems with a quantitative Python-based approach, implement algorithms, interpret results, and present effective solutions. The skills acquired complement the theoretical training provided by other courses, facilitating professional entry into actuarial or risk management roles and enabling access to advanced training programs.

Educational objectives

The final thesis of the Master’s degree consists of the preparation and discussion of a dissertation, which demonstrates the student’s acquisition of advanced knowledge and specialized skills in quantitative models, computational techniques, and their application to financial, insurance, and risk management problems. The thesis represents an in-depth methodological and experimental experience, in which the student integrates knowledge acquired across the program, engages with scientific literature, and produces an original contribution on the chosen topic. Students will develop the ability to analyze complex phenomena, select and apply the most appropriate quantitative model, collect and process data, and interpret market and risk scenarios using digital tools such as Excel, dashboards, simulators, and dedicated software. The thesis work also fosters collaborative skills through interactions with faculty and peers and strengthens the ability to communicate analysis results clearly, concisely, and technically, with particular attention to presenting and arguing investment, hedging, and risk management strategies. At the end of the program, students will be able to independently update their quantitative and regulatory skills according to market developments and evolving risk management techniques. The thesis certifies scientific maturity and critical autonomy necessary to pursue advanced studies, such as second-level Master’s programs and PhDs, and provides solid preparation for the actuarial professional exam or professional entry into complex financial and insurance contexts.

Educational objectives

The educational regulations 270 provide, within each Degree Program, a specific number of credits to be allocated to "student's elective activities." The number of credits provided for this course is 9. These activities consist of exams related to modules offered in the Master's Degree programs of the Faculty or other Faculties at Sapienza.

Educational objectives

The final thesis of the Master’s degree consists of the preparation and discussion of a dissertation, which demonstrates the student’s acquisition of advanced knowledge and specialized skills in quantitative models, computational techniques, and their application to financial, insurance, and risk management problems. The thesis represents an in-depth methodological and experimental experience, in which the student integrates knowledge acquired across the program, engages with scientific literature, and produces an original contribution on the chosen topic. Students will develop the ability to analyze complex phenomena, select and apply the most appropriate quantitative model, collect and process data, and interpret market and risk scenarios using digital tools such as Excel, dashboards, simulators, and dedicated software. The thesis work also fosters collaborative skills through interactions with faculty and peers and strengthens the ability to communicate analysis results clearly, concisely, and technically, with particular attention to presenting and arguing investment, hedging, and risk management strategies. At the end of the program, students will be able to independently update their quantitative and regulatory skills according to market developments and evolving risk management techniques. The thesis certifies scientific maturity and critical autonomy necessary to pursue advanced studies, such as second-level Master’s programs and PhDs, and provides solid preparation for the actuarial professional exam or professional entry into complex financial and insurance contexts.