Learning objectives
a) Knowledge and understanding. The course extends and complements the quantitative skills imparted by the previous teachings. In particular, it provides expertise on the main statistical methods for the analysis of financial phenomena of various kinds and deepens the problems of parameter estimation and diagnostics for a statistical model selection. These techniques include: the logistic regression model for credit risk; Markov models for linear and non-linear financial time series and diagnostic graphics. Participation in educational activities, in conjunction with the exercises of kit, enhance the student's ability to develop, independently, that type of "statistic" that characterizes the nature of the Master of Science in Finance and Risk Management.
b) Applying knowledge and understanding At the end of the course, the student will be able to implement on their own the advanced modeling techniques above. The student will have therefore developed specific skills advanced, they are associated with critical skills for diagnostic, which are essential ingredients in building a good statistical model, with the possible help of appropriate information systems.
c) making judgements. At the end of the course, the student will be able to perform independently all the considerations regarding the financial problems of various kinds. In addition, the student will be able to correctly interpret the results of such analyzes, even when made by other users or experts. By studying the contents of the course, the student matures, therefore, a high degree of autonomy aimed at the correct judgment of the application of proper technique and the associated ability to rework the quantitative knowledge acquired, in order to maximize the relevant information in the content start key risk managment.
d) communication skills. At the end of the course, the student will be able to interact constructively with the financial figures of each profile. The ability to summarize the statistical information of a complex nature, providing, in addition, effective quantitative synthesis, allows the student to contribute their thoughts to the development and drafting of the decision-making processes.
e) learning skills. We wish to give to the student the opportunity to assimilate the key results of the theory of mathematics, statistics and probability that underlie the construction of a statistical model. At the end of the course, the student will have acquired the key concepts to be able to accurately use quantitative tools, if they become necessary in the solution of concrete problems of a financial nature.
Prerequisites
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Course unit content
The course presents the main statistical methods and analysis of financial data for the management of risk and expected thunderstorms:
1) The statistical model and the likelihood function: Parametric models for independent components, maximum likelihood estimators and asymptotic properties;
2) The logistic regression model for credit risk;
3) Linear models for time series, elements of Markov chains and ARMA processes for stationary series;
4) Non-Linear Models: Models ARCH (1) and GARCH (1,1) an outline to their generalizations.
5) Basic introductory tools of technical analysis for trading signals.
The basic theory necessary to understand the use of methodologies and awareness for mastering with the results, will be accompanied by exercises in the classroom, with both probabilistic and computational features using Excel, R and GRETL.
Full programme
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Bibliography
1) Main references
a. Theoretical notes covering contents 1, 3, 4 and 5 of the above program (in preparation).
b. Content 2 has its own book reference:
Cerioli, A. e Laurini, F. (2019) Il modello di regressione logistica per le applicazioni aziendali, Uni-Nova.
c. For all points 1-5 of the program notes with exercises and solutions (in preparation).
2) Preliminary reference for students with minor gaps
Laurini, F. (2012) "Elementi di Analisi delle Serie Storiche Finanziarie". Libreria Medico Scientifica. (Italian)
3) Further references
a) Azzalini, A. (2001) "Inferenza Statistica: Un'introduzione Basata Sul Concetto Di Verosimiglianza". Unitext / Collana Di Statistica E Probabilità Applicata. Springer, seconda edizione. (Italian)
b) Harvey, A.C. (1993) "Time series models". Cambridge, MA: MIT Press, seconda edizione. (English)
c) Tsay, R.S. (2010) "Analysis of Financial Time Series". Wiley-Interscience, terza edizione. (English)
Teaching methods
CASE 1 – STANDARD PHYSICAL IN CLASSROOM (NON COVID PANDEMIC)
Lectures, exercises and preparation of a report / project on trading.
Lectures are frontal and are designed to present the fundamental aspects of statistical and probabilistic models, as well as their numerical estimates and associated uncertainty measurements.
The exercises are carried out individually by the students, after the lessons. The Professor regularly suggests problems and / or exercises to be performed independently outside the classroom hours so that the student can independently assess the degree of learning of the fundamental concepts presented in the classroom during the lectures.
The report is optional and involves the building of trading system. The report is made outside class-time. The report is about ten pages long which include software codes and data. The report is structured discussing critically choices and building bricks for the model selection and diagnostics of the goodness of fit. The maximum mark obtainable with the report is up to 5/30.
CASE 2 – REMOTE ONLINE WITH ZOOM (COVID PANDEMIC)
Online lectures. Part of the lectures will be pre-recorded, part will be live (and will be anyhow recorded). Links to pre-recorded videos will be made available. Live sessions will be recorded (and links will be available).
Active participation to live sessions by students is welcomed.
Students are warmly recommended to attend online classes (both pre-recorded and live) sticking to the lecture plan.
Assessment methods and criteria
CASE 1 – STANDARD PHYSICAL IN CLASSROOM (NON COVID PANDEMIC)
Written exam with oral supplementation optional.
The assessment is via a written test where knowledge and ability of communication with proper technical language are evaluated.
The test is carried in one single occasion. There are 3 exercises with a suitable mix of theory and practice. The test is made by 3 exercise. In general, there are 2 longer exercises worth approximately 10-12 out of 30. The remining exercise is mostly theoretical and it is worth from 6 to 8 out of 30. Time available 45 minutes.
Mark cum laude is obtained wen all parts of the exam are excellent in terms of completeness, clarity and organization, with interdisciplinary connections made explicit.
Electronic devices such as smartphones, smartwatches or tablets are forbidden
It is admitted the usage of a pocket calculator. Calculator with a connection chipset (like for WiFi or Bluetooth) must be used offline aero-mode. An intrusion detection software will be running to send alert signal whenever an active connection is detected.
Students will get an email, with the final mark, to their registered email account (vie the ESSE3 websystem). Students might reject the mark following exclusively an explicit online procedure.
CASE 2 – REMOTE ONLINE WITH ZOOM (COVID PANDEMIC)
Written exam with oral supplementation optional.
The assessment is via a written test where knowledge and ability of communication with proper technical language are evaluated.
The test is carried in one single occasion. There are 3 exercises with a suitable mix of theory and practice. The test is made by 3 exercise. In general, there are 2 longer exercises worth approximately 10-12 out of 30. The remining exercise is mostly theoretical and it is worth from 6 to 8 out of 30. The time available is 45 minutes.
Mark cum laude is obtained wen all parts of the exam are excellent in terms of completeness, clarity and organization, with interdisciplinary connections made explicit.
Electronic devices such as smartphones, smartwatches or tablets are forbidden
During the written test any official textbook can be used. A pocket calculator should also be used.
Students will get an email, with the final mark, to their registered email account (vie the ESSE3 websystem). Students might reject the mark following exclusively an explicit online procedure.
IN ANY CASE (COVID PANDEMIC / NON COVID PANDEMIC)
The oral exam (optional) is available to students that scored at least 18/30 in the written test. There will be a couple of theoretical questions to be answered in about 10 minutes. Depending on the student’s performance, the mark obtained in the test is adjusted +/- 3 points.
Other information
There are 4 or 6 extra hours with Seminars on technical analysis held by an expert. Some of the topics are subject to be examined during the test.
Many lectures will be recorded so that students can listen and see at home what was detailed in the classroom. Additionally, this will help non-attending students to have better understand some topics while preparing for the exam.
Links at recordings will be also in the elly platform.
