generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH … than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is …

forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average …

investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects … both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non …

, including principal component analysis, GARCH-family model, and LASSO regression. The results of this paper suggest that the …

markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance … models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across …

several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized … orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An …

autoregressive conditional heteroskedasticity (GARCH)-class models in terms of their in-sample and out-of-sample forecasting accuracy … 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class …

important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest …

In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option … BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use of the symmetric GARCH option pricing model. The … results indicate that the GARCH option pricing model produces accurate European option prices when compared to market prices …