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Exponential GARCH Modeling
This model uses several realized volatility measures to model a return series. Exponential GARCH highlights the dynamic attributes of the realized and return measures. It is also known for dependence modeling between volatility and returns. EGARCH is a bit different from the normal GARCH method. It uses conditional variance to estimate volatility. This volatility is an explicit multiplicative function for innovations that are lagged. In contrast, the volatility of the normal GARCH causes an intricate functional dependency of innovations. This volatility is an additive function of the lagged error terms.
Autoregressive Integrated Moving Average (ARIMA) Model
This model is a regression type of analysis that measures the strength of a single dependent variable with other changing variables. This model is often used in financial time series to forecast moves in financial markets. ARIMA examines the values in the series rather than the actual values. The components of ARIMA are:
A type of model that provides information on a changing variable that regresses on previous or lagged values.
This is the process of differencing raw observations to ensure that the time series becomes stationary. Meaning, the difference between the previous values and the data values is used to replace the data values.
Moving average integrates the dependency between observation and residual error from the model.
Autocorrelation is the level of correlation of the same variables between two successive intervals of time. It is used to measure how the prior version of a variable’s value is associated with its time series original version. In statistics, the concept of autocorrelation is also referred to as serial correlation. It is always used with both ARMA and ARIMA models.