Autoregression and ARCH GARCH models

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SUMMARY

The discussion centers on understanding ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, with a specific inquiry about the relevance of autoregression as a foundational concept. The participant, who holds a master's degree in Mathematics and Physics, seeks resources to aid in their study of these statistical models. A recommended resource is provided, specifically a link to a website that offers insights into time series analysis.

PREREQUISITES
  • Understanding of basic statistics and probability
  • Familiarity with autoregressive models
  • Knowledge of time series analysis concepts
  • Mathematical proficiency in calculus and linear algebra
NEXT STEPS
  • Study the fundamentals of autoregressive models
  • Explore ARCH and GARCH model applications in financial time series
  • Read "Time Series Analysis" by Box, Jenkins, and Reinsel
  • Learn about model diagnostics and validation techniques for time series
USEFUL FOR

Statisticians, financial analysts, data scientists, and anyone interested in advanced time series modeling techniques.

hayyan1
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Hello guys, I need to understand the ARCH and GARCH models and so I need some advice on where I should begin my study. In order to understand ARCH models, am I right to begin with studying autoregression? If so can you guys provide me with a link or book I should look into...

By the way, I have a masters degree in Mathematics and Physics, but I have not covered any statistics in my degree. This shouldn't be a problem, I don't think, because I know the basics of stats and probability.

Thanks
 
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Thanks Ill check that out
 

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