A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global...

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, designed to model volatility clustering, exhibits heavy-tailedness regardless of the distribution of its innovation term. When applying the model to financial time series, the distribution of innovations plays an...

This paper considers 100 stocks from 10 sectors over the period 2000 to 2010 to examine the impact of market conditions, sector type and horizon on the forecasting performance of various volatility models. Out-of-sample forecasts are generated with standard daily GARCH and with ARFIMA/HAR models...

This paper considers the short- and long-memory linear processes with GARCH (1,1) noises. The functional limit distributions of the partial sum and the sample autocovariances are derived when the tail index α is in (0,2), equal to 2, and in (2,∞), respectively. The partial sum weakly...

In this study, volatility of sock return behavior through a regime-Switching Asymmetric Power GARCH Model (RS-APGARCH) analyses in Istanbul Stock Exchange (ISE), Turkey, during the period of 1988-2006 and show that ISE's asymmetric response and the intensity of this response to good and/or bad...

The paper investigates from an empirical perspective aspects related to the occurrence of the IGARCH effect and to its impact on volatility forecasting. It reports the results of a detailed analysis of twelve samples of returns on financial indexes from major economies (Australia, Austria,...

This paper studies the integrated Garch (IGARCH) e®ect, a phenomenon often encountered when estimating conditional auto-regressive models on ¯nancial time series. The analysis of twelve indexes of major ¯nancial markets provides empirical evidence of its well-spread presence especially in...