Lecture

Long Memory and ARCH: Time Series Math 342

In course

In ut elit elit aliqua. Sunt veniam laborum culpa tempor deserunt duis nulla. Qui labore aliqua labore esse eu nisi eu amet minim tempor voluptate duis. Non id excepteur enim velit consectetur. Duis consequat est cupidatat cupidatat sunt anim cillum Lorem cupidatat et. Ipsum minim mollit consectetur consectetur commodo consequat elit esse pariatur. Duis tempor anim aliquip laborum tempor laborum.

Description

This lecture covers the concept of long memory in time series, where correlations vary slowly as time increases. It explores covariance models with polynomial decay, spectral behavior, and the FARIMA class. Additionally, it delves into Autoregressive Conditional Heteroskedasticity (ARCH) processes, inspired by financial time series, focusing on log-returns, conditional mean, and variance estimation.

Instructor

elit proident ea

Ipsum reprehenderit dolore ad officia et aliqua. Magna nostrud velit sit dolor. Lorem dolore cupidatat occaecat deserunt amet ullamco enim dolor laborum sunt ut commodo esse esse.

Official source

https://mediaspace.epfl.ch/media/0_cc2c3i3h

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Data analysis: Time series

Economics

Microeconomics: Financial economics

Related lectures (45)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.