Maximum Likelihood Estimates for Positive Valued Dynamic Score Models; The DySco Package, Philipp Andres, Computational Statistics & Data Analysis, Vol 76, pp. 34-42 (2014)
Abstract:
Recently, the Dynamic Conditional Score (DCS) or Generalized Autoregressive Score (GAS) time series models have attracted considerable attention. This motivates the need for a software package to estimate and evaluate these new models. A straightforward to operate program called the Dynamic Score (DySco) package is introduced for estimating models for positive variables, in which the location/scale evolves over time. Its capabilities are demonstrated using a financial application.
EGARCH Models with Fat Tails, Skewness and Leverage, Andrew Harvey and Genaro Sucarrat, Computational Statistics & Data Analysis, Vol. 76 pp. 320-338 (2014)
An EGARCH model in which the conditional distribution is heavy-tailed and skewed is proposed. The properties of the model, including unconditional moments, autocorrelations and the asymptotic distribution of the maximum likelihood estimator, are set out. Evidence for skewness in a conditional tt-distribution is found for a range of returns series, and the model is shown to give a better fit than comparable skewed-tt GARCH models in nearly all cases. A two-component model gives further gains in goodness of fit and is able to mimic the long memory pattern displayed in the autocorrelations of the absolute values.