Lecture

Financial Time Series: ARCH and GARCH Models

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In course

FIN-417: Quantitative financial risk management

This course is an introduction to quantitative risk management that covers standard statistical methods, multivariate models, risk measures, non-linear dependence structures (copula models), as well as credit risk. ...

Description

This lecture covers regression analysis, constructing larger matrices, multivariate linear regression, principal component analysis, spectral decomposition theorem, principal components transform, and factor models. It delves into finding uncorrelated linear combinations of data, PCA as a data rotation technique, and the aim to account for most data variability.

Instructors (2)

Semyon Malamud

Teaching & PhD PhD Students Johannes Schwab, Onur Demiray, Giuseppe Matera Past EPFL PhD Students Rémy Praz, Evgeny Petrov, Yuan Zhang, Julien Arsène Blatt, Erik Hapnes, Teng Andrea Xu, Boris Kuznetsov Courses Big Data and Machine Learning for Financial Economics FIN-622 This class is an introduction to Machine Learning and High Dimensional Statistics in Finance. We start with purely empirical approach, focusing first on high dimensional regressions then moving to kernel methods and deep l

Damir Filipovic

Damir Filipovic holds the Swissquote Chair in Quantitative Finance and is Swiss Finance Institute Professor at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Prior to this, he was head of the Vienna Institute of Finance and professor at the University of Vienna. He previously held the chair of financial and insurance mathematics at the University of Munich, and he was on the faculty of Princeton University. He received his Ph.D. in mathematics from ETH Zurich in 2000. Damir Filipovic worked as a scientific consultant for the Swiss Federal Office of Private Insurance from 2003 to 2004. There he co-developed the Swiss Solvency Test, which defines the regulatory capital requirement for all Swiss based insurance companies and groups. He is on the editorial board of several academic journals. His research interests include the term structure of interest rates, credit and volatility risk, quantitative methods in risk management, and stochastic processes. His papers have been published in a variety of academic journals including the Journal of Financial Economics, Mathematical Finance, Finance and Stochastics, and the Annals of Applied Probability. He is the author of a textbook titled Term-Structure Models.

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (32)

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Kernel principal component analysis

In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space. Recall that conventional PCA operates on zero-centered data; that is, where is one of the multivariate observations.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

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