The EGARCH_model.xlsx Model


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Given a series of observations, we want to estimate the data generation process, allowing variance to vary over time following a first order exponential generalized autoregressive conditional heteroscedasticity model (i.e. EGARCH(p,q), p=1 & q=1) that accounts for the asymmetry. The asymmetry here refers to difference in behavior of residuals for positive and negative shocks.


Econometrics | Forecasting | Time Series | Maximum Likelihood | Time Varying Parameters | Heteroscedasticity | Volatility Modeling | EGARCH |