GARCH 1.1
GARCH stands for heteroscedastic conditional generalized autoregressive model.
In other words, the GARCH model finds the average volatility in the medium term through an autoregression that depends on the sum of the lagged shocks and the sum of the lagged variances.
The GARCH model and its extensions are used for their ability to predict volatility in the short to medium term.
This script was developed to predict the volatility of stock options in real time and indicate a reference volatility through the application of a percentage reducer, which can be changed by the user depending on his operating model.
- Generalized because it takes into account recent and historical observations.
- Autoregressive because the dependent variable returns on itself.
- Conditional because future variation depends on historical variation.
- Heteroscedastic because the variance varies as a function of the observations.
In other words, the GARCH model finds the average volatility in the medium term through an autoregression that depends on the sum of the lagged shocks and the sum of the lagged variances.
The GARCH model and its extensions are used for their ability to predict volatility in the short to medium term.
This script was developed to predict the volatility of stock options in real time and indicate a reference volatility through the application of a percentage reducer, which can be changed by the user depending on his operating model.