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When should I use GARCH model?
GARCH is a statistical model that can be used to analyze a number of different types of financial data, for instance, macroeconomic data. Financial institutions typically use this model to estimate the volatility of returns for stocks, bonds, and market indices.
Does GARCH require stationarity?
The GARCH(1,1) process is stationary if the stationarity condition holds. ARCH model can be estimated by both OLS and ML method, whereas GARCH model has to be estimated by ML method.
Why do we use ARMA models?
Applications. ARMA is appropriate when a system is a function of a series of unobserved shocks (the MA or moving average part) as well as its own behavior. For example, stock prices may be shocked by fundamental information as well as exhibiting technical trending and mean-reversion effects due to market participants.
Is the ARMA model the same as the GARCH model?
Having said this, the ARMA model may be considered as appropriate tool for understanding and assessing the dependence and the causal structure and to better find the predictions of the future values in each time series. If you confuse between ARMA and GARCH models, i believe that the following link may help you: Good luck. Thank you very much!
What do you need to know about ARMA models?
ARMA models the conditional mean and GARCH models conditional variance. Firstly, you need to do preliminary analysis of your time series data, summary statistic, acf, pacf, unit root test, jb or sw test of normality.
When to use Arma-GARCH for macroeconomic forecasts?
If you want to use ARMA-GARCH for forecasting macroeconomic series, you should check for stationarity. An alternative is to remove the trend using some break tests and then you apply your ARMA-GARCH on the cycle component. This technique can give you a good forecasts. Best regards. I’d like to thank everyone for the kind response!
Can a GARCH model be used to estimate var?
To estimate VaR using conditional variance (GARCH), it is important to have stable GARCH model, which requires stationarity of residuals, otherwise you can use conditional volatility models with infinite variance, or may be extreme value theory which requires a large set of high frequency data.