processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are … separately. The primary result of the paper is that the volatility is best modelled using a GARCH process and that an ARMA … more volatile. The main aim of the paper is to test whether a model estimated on data with lower volatility can be used in …

We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory … in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid … that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH …

This paper contains a survey of univariate models of conditional heteroskedasticity. The classical ARCH model is … mentioned, and various extensions of the standard GARCH model are highlighted. This includes the Exponential GARCH model …. Stochastic volatility models remain outside this review. …

We use an information-theoretic approach to interpret Engle's (1982) and Bollerslev's (1986) GARCH model as a model for … may be generalized, if we use alternative measures of volatility. We choose one feasible alternative and derive a … generalized volatility model. Applying this model to some exemplary market indices, we are able to give some empirical evidence …

This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH …, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock … price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of …

ARCH modelling framework of Engle (1982) and its GARCH generalization of Bollerslev (1986) gave a huge impetus to … describe the most typical features of capital markets like volatility clustering, excess kurtosis and fat tails. As empirical … evidence shows asymmetry is also a prominent feature of stock market returns volatility. The reaction of risk if stock returns …

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 … that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative …

-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series … the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID … returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence …