A model in which today's variance depends on the previous variance and the latest squared return. It captures volatility clustering and the return to a long-run level.
Where you will meet it
GARCH comes up in 5 learning outcome notes across the FRM syllabus.
A few of them:
Measuring and Monitoring Volatility FRM Part I · Valuation and risk models
“GARCH(1,1) also has declining weights and adds a constant, so variance reverts toward a long-run value, at the cost of estimating three parameters by maximum likelihood.”
Non-Parametric Approaches FRM Part II · Market risk measurement and management
“Filtered historical simulation fits a model such as GARCH, standardises returns into residuals, resamples those residuals and rescales them by forecast volatility, which also allows multi-day paths.”
Measuring and Monitoring Volatility FRM Part I · Valuation and risk models
“GARCH(1,1) models the conditional variance each day from the latest return and the previous variance.”
Measuring and Monitoring Volatility FRM Part I · Valuation and risk models
“Under GARCH(1,1), the expected variance k days ahead is: Forecast = V + (α + β)^k × (current variance − V).”
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