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Using GARCH (1,1) Approach to Estimate Volatility

FRM Exam, Risk Management

This lesson is part 6 of 8 in the course 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.

Previous Lesson

‹ Volatility: Exponentially Weighted Moving Average (EWMA)

Next Lesson

How to Forecast Volatility Using GARCH (1,1) ›

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In this Course

  • How to Calculate Historical Volatility
  • Approaches to Estimating Volatility
  • Using Excel’s Goal Seek Function to Estimate Implied Volatility
  • Volatility: Moving Average Approaches
  • Volatility: Exponentially Weighted Moving Average (EWMA)
  • Using GARCH (1,1) Approach to Estimate Volatility
  • How to Forecast Volatility Using GARCH (1,1)
  • Calculate Historical Volatility Using EWMA

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