PROTECTED SOURCE SCRIPT
GARCH 1.1
GARCH stands for heteroscedastic conditional generalized autoregressive model.
The GARCH model is a generalized autoregressive model that captures volatility clusters of returns through conditional variance.
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.
The GARCH model is a generalized autoregressive model that captures volatility clusters of returns through conditional variance.
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.
受保护脚本
此脚本以闭源形式发布。 但是，您可以自由使用它，没有任何限制 — 在此处了解更多信息。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。
受保护脚本
此脚本以闭源形式发布。 但是，您可以自由使用它，没有任何限制 — 在此处了解更多信息。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。