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Generalized AutoRegressive Conditional Heteroskedasticity – GARCH

Generalized AutoRegressive Conditional Heteroskedasticity - GARCH

What is 'Generalized AutoRegressive Conditional Heteroskedasticity (GARCH)'

A statistical model used by financial institutions to estimate the volatility of stock returns. This information is used by banks to help determine what stocks will potentially provide higher returns, as well as to forecast the returns of current investments to help in the budgeting process.

Explaining 'Generalized AutoRegressive Conditional Heteroskedasticity (GARCH)'

There are many variations of GARCH, including NGARCH to include correlation, and IGARCH which restricts the volatility parameter. Each model can be used to accomodate the specific qualities of the stock, industry or economic state.


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Q&A About Generalized AutoRegressive Conditional Heteroskedasticity – GARCH


What do banks use GARCH for?

Banks use GARCH to determine which stocks will potentially provide higher returns as well as forecast the returns of current investments.

Which variation can be used to accommodate specific qualities of a stock, industry or economic state?

IGARCH can be used to accomodate specific qualities.

What is GARCH?

GARCH is a statistical model used by financial institutions to estimate the volatility of stock returns.

How many variations are there of GARCH?

There are several variations including NGARCH and IGARCH.