Using GARCH (1,1) Approach to Estimate Volatility

This video provides an introduction to the GARCH approach to estimating volatility, i.e., Generalized AutoRegressive Conditional Heteroskedasticity.

GARCH is a preferred method for finance professionals as it provides a more real-life estimate while predicting parameters such as volatility, prices and returns.

GARCH(1,1) estimates volatility in a similar way to EWMA (i.e., by conditioning on new information) except that it adds a term for mean reversion. It says the series is "sticky" or somewhat persistent to a long-run average.

This video is developed by David from Bionic Turtle.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.