This thesis is concerned with volatility estimation using financial panels and bias-reduction in non-linear dynamic panels in the presence of dependence.
Traditional GARCH-type volatility models require large time-series for accurate estimation. This makes it impossible to analyse some interesting datasets which do not have a large enough history of observations. This study contributes to the literature by introducing the GARCH Panel mod ... [truncated at 450 characters in length]
|Key phrase||nonlinear dynamic panel data bias reduction GARCH hedge funds composite likelihood|