We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with...

We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with...

The asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of Sentana (1995). We introduce a framework for testing asymmetries...

The paper advances the log-generalized gamma distribution as a suitable generator of conditional skewness. Based on the NYSE composite daily returns an asMA-asQGARCH model along with skewness dynamics is estimated. The results indicate a skewness that varies between sizeable negative skewness...

A Self-Exciting Threshold AutoRegressive (SETAR) model is applied to the Italian stock market volatility, to obtain volatility forecasts and Value-at-Risk (VaR) estimates. There is almost nothing dealing with Italian markets in the literature of Threshold models, which have never been used for...

The paper undertakes a non-parametric analysis of the very high frequency movements in stock market volatility using very finely sampled data on the Samp;P VIX index compiled by the CBOE. The data suggest that stock market volatility is best described as a pure jump process without a continuous...

We develop a portfolio risk model that uses high-frequency data to forecast the loss surface, which is the set of loss distributions at future time horizons. Our model uses a fully automated, semi-parametric fitting procedure that has its basis in extreme value statistics. We take account of...

Time reversal invariance can be sumarised as follows: no difference can be measured if a sequence of events is run forward or backward in time. Because price time series are dominated by a randomness that hides possible structures and orders, the existence of time reversal invariance requires...

Motivated by the implications from a stylized self-contained general equilibrium model incorporating the effects of time-varying economic uncertainty, we show that the difference between implied and realized variation, or the variance risk premium, is able to explain a non-trivial fraction of...