We are using GARCH model for checking the volatility of time series data. How can we check the Economic significance of the model ? Especially the extent to which the independent variable contribute to volatility of the model (ht) in each period.
Dear Abdul Rishad,
ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. Attached article may help you to understand more about the economic significance of GARCH model.
Best Wishes,
Dear Abdul Rishad,
ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. Attached article may help you to understand more about the economic significance of GARCH model.
Best Wishes,
GARCH model usually produces results for two equations. one is mean equation and another one is variance equation.
the coefficient of the indepedent variable in mean equation indicate the magnitude and direction of impact of the concerned predictor on the mean returns
On the other hand, variance equation has two variables ARCH and GARCH. in GARCH(1,1) model, ARCH indicates the impact of recent news on the volatility and GARCH indicates the impact of old news on the volatility.
the sum of the coefficients of ARCH and GARCH denote the persistence of volatility.
the contribution of independent variabes (i.e., ARCH and GARCH) can be observed from the values of the coefficients of these two variables.
i hope this reply is useful to you .
if you need any further clarification, please discuss.
You can include an external regressor within the equation of conditional volatility (ht), that external regressor is the volatility of the exogenous variable with which you intend to explain the volatility of the endogenous variable. After estimating the new equation of (ht) using a maximum likelihood method, you can look at the statistical significance and magnitude of the coefficient associated with that exogenous variable.
If you need information about the implementation in R or MatLab do not hesitate to contact me
Being that your interest is on assessing the impact of an independent variable on the volatility of the model, then the independent variable(s) should be included in the variance equation rather than in the mean equation, and the associated coefficient from the output of the should give the required results.
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