Lecture

ARCH and GARCH Models

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In course

FIN-407: Machine learning in finance

This course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization, return prediction, and textual analysis. A particular focus will be on deep learning and the practical details o ...

Description

This lecture covers ARCH and GARCH models, focusing on volatility clustering and time series models. It explains the concepts, equations, and applications of these models in finance and macroeconomics. The instructor discusses the estimation, calibration, and filtering steps, including Kalman, Extended Kalman, and Unscented Kalman filters, as well as the Particle filter. Practical considerations and examples of applications are also presented.

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Instructor

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

Official source

Ontological neighbourhood

Economics

Microeconomics: Financial economics

Related concepts (32)

Stochastic volatility

In statistics, stochastic volatility models are those in which the variance of a stochastic process is itself randomly distributed. They are used in the field of mathematical finance to evaluate derivative securities, such as options. The name derives from the models' treatment of the underlying security's volatility as a random process, governed by state variables such as the price level of the underlying security, the tendency of volatility to revert to some long-run mean value, and the variance of the volatility process itself, among others.

Macroeconomic model

A macroeconomic model is an analytical tool designed to describe the operation of the problems of economy of a country or a region. These models are usually designed to examine the comparative statics and dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, and the level of prices. Macroeconomic models may be logical, mathematical, and/or computational; the different types of macroeconomic models serve different purposes and have different advantages and disadvantages.

Microfoundations

Microfoundations are an effort to understand macroeconomic phenomena in terms of economic agents' behaviors and their interactions. Research in microfoundations explores the link between macroeconomic and microeconomic principles in order to explore the aggregate relationships in macroeconomic models. During recent decades, macroeconomists have attempted to combine microeconomic models of individual behaviour to derive the relationships between macroeconomic variables.

Financial modeling

Financial modeling is the task of building an abstract representation (a model) of a real world financial situation. This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project, or any other investment. Typically, then, financial modeling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature. It is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions.

Kalman filter

For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

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