Statistical Methods for Finance
A.Y. 2024/2025
Learning objectives
The main goal of this course is to give students the necessary statistical instruments required to deal with multivariate data in modern quantitative finance, focusing in particular on multivariate probability distributions and dependence measures.
The course will first introduce and review the general basic concepts related to multivariate random variables and then will analyze some multivariate models that have found wide application in quantitative finance (multivariate normal model and generalizations thereof). Then, the course will present copulas as a statistical tool for building flexible multivariate models and defining new dependence measures that can better fit and explain specific features present in financial data.
The course will first introduce and review the general basic concepts related to multivariate random variables and then will analyze some multivariate models that have found wide application in quantitative finance (multivariate normal model and generalizations thereof). Then, the course will present copulas as a statistical tool for building flexible multivariate models and defining new dependence measures that can better fit and explain specific features present in financial data.
Expected learning outcomes
At the end of the course, the student should know the basic theory of multivariate random variables and the genesis and properties of some noteworthy families of probability distributions, such as the multivariate normal distribution, the multivariate normal variance mixtures, the spherical and elliptical distributions. The student should be also acquainted with the concept of copula and its use in the construction of multivariate distribution, and with copula-based dependence measures which overtake the shortcomings of Pearson's correlation coefficient.
The students is expected to be able to apply this theoretical knowledge by evaluating the applicability of different models from a scientific perspective and choosing the most appropriate distribution for modeling multivariate data in the financial field.
The students is expected to be able to apply this theoretical knowledge by evaluating the applicability of different models from a scientific perspective and choosing the most appropriate distribution for modeling multivariate data in the financial field.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
1.Review of basic concepts for univariate and bivariate random variables.
Basic notions of univariate random variables; gallery of common random distributions: Bernoulli, Binomial, normal, exponential, beta, gamma. Skewness and Kurtosis (leptokurtic, mesokurtic and platykurtic distributions). Generalized inverse function and quantile function. Transformation of random variables: methods for recovering the pdf/cdf of a function of a random variable (univariate case). The case of monotone functions; the case of Y=X^2. Bivariate distributions, continuous case: bivariate density function, bivariate cumulative distribution function, marginal density functions and cumulative distribution functions. Characteristic function: definition and main properties. Characteristic function for the normal rv and for the sum of independent normal rvs. Inversion theorems. Property of pdf and cf for symmetrical distributions. Properties and asymptotic distribution of the ecdf. Kolmogorov-Smirnov test. (Stable distributions: definition, parametrization, generalization of central limit theorem, link with infinite divisibility.)
2.Standard multivariate models.
Introduction to multivariate models: joint, marginal and conditional distributions; independence; moments (mean vector and covariance and correlation matrices); linear transformations. Standard estimators of the mean vector and of covariance and correlation matrices. The multivariate normal distribution. Definition/construction. Joint probability density function. Stochastic simulation. Properties of the multivariate normal distribution: linear transformations, marginal distributions, conditional distributions, quadratic form, convolution. The bivariate case: joint density function, conditional distributions, joint cumulative distribution function (quadrant probability). Testing normality: 1) univariate case: QQplot; Jarque-Bera and Shapiro-Wilk tests 2) multivariate case: Mahalanobis distance and its asymptotic distribution. Multivariate skewness and kurtosis; Mardia test. Weaknesses of the multivariate normal model. Mixture models: generalities (finite mixtures and compound distributions). Multivariate normal variance mixture models: genesis and first main properties. Characteristic function, linear transformation, density, uncorrelation/independence for multivariate variance mixture models. The univariate and multivariate Student's t distribution. Spherical distributions: definitions and characterizations, also in terms of rvs R and S. Joint density of a spherical rv. Elliptical distributions: definition. Properties: stochastic representation, characteristic function, linear operations, marginal distributions, conditional distributions, convolutions, quadratic form. Estimating the location vector and dispersion matrix. Testing for elliptical symmetry: QQplots and numerical tests.
3.Copulas.
Copulas: introduction, definition, and basic properties. Quantile transformation and probability transformation. Sklar's theorem. Invariance of copulas for strictly increasing transformations. Frechét lower and upper bounds. Example of copulas: fundamental copulas (independence copula, comonotonicity copula, countermonotonicity copula); implicit copulas (Gaussian and t copulas). Examples of explicit copulas (Gumbel and Clayton). Meta-distributions: joining arbitrary margins together through a copula; simulation of meta distributions. Survival copulas. Radial Symmetry. Conditional distributions of copulas. Copula density. Exchangeability. Perfect dependence: comonotonicity and countermonotonicity. Dependence Measures. Pearson's correlation: definition. First fallacy of Pearson's rho: The marginal distributions and pairwise correlations of a random vector do not determine its joint distribution. Second fallacy of Pearson's rho: For two given univariate margins and a correlation coefficient in [-1,+1] it is not always possible to construct a joint distribution with those margins and that rho. Attainable correlations for rho. Examples. Kendall's tau; Spearman's rho: definitions and main properties; relationship with the copula C of a bivariate random vector. Relationship between Pearson's rho, Kendall's tau and Spearman's rho for the Gaussian copula. Coefficients of upper and lower tail dependence: definition and their relation to the copula C of a bivariate random vector. Fitting copulas to data. The method-of-moments approach. The maximum likelihood method and the two-step approach. Step 1: estimating the margins (parametrically or non-parametrically), step 2: estimating the copula parameter via pseudo-sample from the copula. Examples: estimating the Gaussian and t copulas. The R package "copula".
Basic notions of univariate random variables; gallery of common random distributions: Bernoulli, Binomial, normal, exponential, beta, gamma. Skewness and Kurtosis (leptokurtic, mesokurtic and platykurtic distributions). Generalized inverse function and quantile function. Transformation of random variables: methods for recovering the pdf/cdf of a function of a random variable (univariate case). The case of monotone functions; the case of Y=X^2. Bivariate distributions, continuous case: bivariate density function, bivariate cumulative distribution function, marginal density functions and cumulative distribution functions. Characteristic function: definition and main properties. Characteristic function for the normal rv and for the sum of independent normal rvs. Inversion theorems. Property of pdf and cf for symmetrical distributions. Properties and asymptotic distribution of the ecdf. Kolmogorov-Smirnov test. (Stable distributions: definition, parametrization, generalization of central limit theorem, link with infinite divisibility.)
2.Standard multivariate models.
Introduction to multivariate models: joint, marginal and conditional distributions; independence; moments (mean vector and covariance and correlation matrices); linear transformations. Standard estimators of the mean vector and of covariance and correlation matrices. The multivariate normal distribution. Definition/construction. Joint probability density function. Stochastic simulation. Properties of the multivariate normal distribution: linear transformations, marginal distributions, conditional distributions, quadratic form, convolution. The bivariate case: joint density function, conditional distributions, joint cumulative distribution function (quadrant probability). Testing normality: 1) univariate case: QQplot; Jarque-Bera and Shapiro-Wilk tests 2) multivariate case: Mahalanobis distance and its asymptotic distribution. Multivariate skewness and kurtosis; Mardia test. Weaknesses of the multivariate normal model. Mixture models: generalities (finite mixtures and compound distributions). Multivariate normal variance mixture models: genesis and first main properties. Characteristic function, linear transformation, density, uncorrelation/independence for multivariate variance mixture models. The univariate and multivariate Student's t distribution. Spherical distributions: definitions and characterizations, also in terms of rvs R and S. Joint density of a spherical rv. Elliptical distributions: definition. Properties: stochastic representation, characteristic function, linear operations, marginal distributions, conditional distributions, convolutions, quadratic form. Estimating the location vector and dispersion matrix. Testing for elliptical symmetry: QQplots and numerical tests.
3.Copulas.
Copulas: introduction, definition, and basic properties. Quantile transformation and probability transformation. Sklar's theorem. Invariance of copulas for strictly increasing transformations. Frechét lower and upper bounds. Example of copulas: fundamental copulas (independence copula, comonotonicity copula, countermonotonicity copula); implicit copulas (Gaussian and t copulas). Examples of explicit copulas (Gumbel and Clayton). Meta-distributions: joining arbitrary margins together through a copula; simulation of meta distributions. Survival copulas. Radial Symmetry. Conditional distributions of copulas. Copula density. Exchangeability. Perfect dependence: comonotonicity and countermonotonicity. Dependence Measures. Pearson's correlation: definition. First fallacy of Pearson's rho: The marginal distributions and pairwise correlations of a random vector do not determine its joint distribution. Second fallacy of Pearson's rho: For two given univariate margins and a correlation coefficient in [-1,+1] it is not always possible to construct a joint distribution with those margins and that rho. Attainable correlations for rho. Examples. Kendall's tau; Spearman's rho: definitions and main properties; relationship with the copula C of a bivariate random vector. Relationship between Pearson's rho, Kendall's tau and Spearman's rho for the Gaussian copula. Coefficients of upper and lower tail dependence: definition and their relation to the copula C of a bivariate random vector. Fitting copulas to data. The method-of-moments approach. The maximum likelihood method and the two-step approach. Step 1: estimating the margins (parametrically or non-parametrically), step 2: estimating the copula parameter via pseudo-sample from the copula. Examples: estimating the Gaussian and t copulas. The R package "copula".
Prerequisites for admission
Students are required to be familiar with linear algebra, differential and integral calculus, the basics of probability theory and inferential statistics, and elemental programming skills.
Teaching methods
Lectures and practical classes. Theoretical classes are always combined with practical experience, consisting of numerical exercises (to be solved by hand) or implementation of theoretical models and methods in the R programming environment.
During the class, the istructor uses the whiteboard and presents the slides he has provided to the students on the Ariel webpage; the instructor uses the PC to illustrate software implementation of statistical models and methods in the R programming environment.
Students are encouraged to test and enhance their skills through the provided supplementary material (mainly solved exercises), past exams, online questionnaries, and suggested bibliographical references.
During the class, the istructor uses the whiteboard and presents the slides he has provided to the students on the Ariel webpage; the instructor uses the PC to illustrate software implementation of statistical models and methods in the R programming environment.
Students are encouraged to test and enhance their skills through the provided supplementary material (mainly solved exercises), past exams, online questionnaries, and suggested bibliographical references.
Teaching Resources
Slide and exercises prepared by the instructor and available here: https://myariel.unimi.it/course/view.php?id=4045
Main references:
A.J. McNeil, R. Frey and P. Embrechts: Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton University Press, 2005
A.J. McNeil, R. Frey and P. Embrechts: Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton University Press, 2nd Edition, 2015
J.-F. Mai, M. Scherer: Financial Engineering with Copulas Explained, Palgrave Macmillan, New York, 2014
Other references:
M. Hofert, I. Kojadinovic, M. Machler, J. Yan: Elements of Copula Modeling with R, Springer, New York, 2018
R.G. Gallager, Stochastic Processes for Applications, Cambridge University Press, 2013
T. Mazzoni, A First Course in Quantitative Finance, Cambridge University Press, 2018
Y. Tse, Nonlife Actuarial Models: Theory, Methods and Evaluation, Cambridge University Press, 2009
Main references:
A.J. McNeil, R. Frey and P. Embrechts: Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton University Press, 2005
A.J. McNeil, R. Frey and P. Embrechts: Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton University Press, 2nd Edition, 2015
J.-F. Mai, M. Scherer: Financial Engineering with Copulas Explained, Palgrave Macmillan, New York, 2014
Other references:
M. Hofert, I. Kojadinovic, M. Machler, J. Yan: Elements of Copula Modeling with R, Springer, New York, 2018
R.G. Gallager, Stochastic Processes for Applications, Cambridge University Press, 2013
T. Mazzoni, A First Course in Quantitative Finance, Cambridge University Press, 2018
Y. Tse, Nonlife Actuarial Models: Theory, Methods and Evaluation, Cambridge University Press, 2009
Assessment methods and Criteria
The final exam is a unique written test that can be taken during any of the official exam dates. It consists of:
- a number (usually 14) of multiple choice questions; for each question, four possible answers are provided of which only one is correct
- one or more theoretical questions with an open answer (say, about 150 words)
- one or more numerical exercises
The questions cover the whole course programme and by balancing theory with practice are hopefully able to check the overall student's competences.
- a number (usually 14) of multiple choice questions; for each question, four possible answers are provided of which only one is correct
- one or more theoretical questions with an open answer (say, about 150 words)
- one or more numerical exercises
The questions cover the whole course programme and by balancing theory with practice are hopefully able to check the overall student's competences.
Educational website(s)
Professor(s)