This study is designed to model and forecast Nigeria's stock market using the AllShare Index (ASI) as a proxy. By … studyanalyzes the stock market volatility in three distinct regimes (accumulation or distri-bution - regime 1; big-move - regime 2 …

the behaviour of returns and their volatility during both the calm as well as various crises/turmoil periods. Besides the …-GJR-GARCH) were estimated in order to examine the volatility switches of the Central European transition stock markets. The t …-distribution of error terms was used to capture the dynamics of analysed returns more precisely. The results proved high volatility …

We examine the performance of volatility models that incorporate features such as long (short) memory, regime …-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models …-share equity indices, bond indices and real estate security indices at the country level. Furthermore, we investigate …

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model …. Using Nigeria All Share Index from January 2, 2008 to February 11, 2013, this study estimates first order symmetric and …

-of-the-week effects in returns and volatility using the Nigerian stock exchange (NSE-30). The Gaussian, Student-t, and the Generalized … are sensitive to error distribution. Our finding also shows that evidence of good or bad news in volatility does not only …

the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We …

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …