is the STAR. In this paper some nonlinear modelling techniques are applied to a Finnish financial time series, the daily … ARCH family, a mean-nonlinear model, namely Smooth Transition Autoregression (STAR)-model and a neural network. Linearity … nonlinearly dependent. Adapting an ARCH(3) eliminates the dependencies most satisfactorily. The ARCH-models and STAR-models were …

impact on expected returns. By using a conditional capital asset pricing model (CAPM) with an asymmetric multivariate GARCH …

Heteroscedasticity (GARCH) models and the Kalman filter method. The three GARCH models applied are: bivariate GARCH, BEKK GARCH, and … GARCH-GJR. Forecast errors based on 20 UK company's weekly stock return (based on time-varying beta) forecasts are employed … to evaluate the out-of-sample forecasting ability of both the GARCH models and the Kalman method. Measures of forecast …

conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described … model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH … model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the …

of the joint existence of breaks and GARCH effects. It proposes a data-driven procedure to credibly identify the number … GARCH effects. However, the presence of structural changes is found to be the primary reason for the non-normality and not … the GARCH effects. Also, there is still some remaining excess kurtosis that is unlikely to be linked to the specification …

from a theoretical and an empirical point of view. In particular a GARCH(1,1) model, an EGARCH(1,1) model and a log … results do not lead to a straightforward preference between GARCH(1,1) and SV, the EGARCH shows the best performance. …

It is shown that the ML estimates of the popular GARCH(1,1) model are significantly negatively biased in small samples … indicate that a high level of persistence in GARCH(1,1) models obtained using a large number of observations has … proposed that at least 250 observations are needed for ARCH(1) models and 500 observations for GARCH(1,1) models. A simple …

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on … effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models … positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH …

In this paper, a set of appropriately modified information criteria for selection of models from the AR-GARCH class is …