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We examine the predictive value of expected skewness of oil returns for the realized volatility using monthly data from 1859:11 to 2023:04. We utilize a quantile predictive regression model, which is able to accommodate nonlinearity and structural breaks. In-sample results show that the...
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Recent research shows that time-varying volatility plays a crucial role in nonlinear modeling. Contributing to this literature, we suggest a DSGE-GARCH approach that allows for straightforward computation of DSGE models with time-varying volatility, where the volatility component is formulated...
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