[PDF] Top 20 Volatility Modelling using ARCH and GARCH Models (A Case study of Exchange Rate in Sudan) (at the period from 2007-2018 )

... variance volatility to exchange rate in Sudan at the period (2007 – 2018) and estimate the variance of exchange rate in Sudan also, and how to forecasting by exchange rate in Sudan by using ARCH and ... See full document

... ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial ...These models are especially useful when the goal of the study is to analyse ... See full document

... Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio ...the volatility characteristics on Jordan’s capital market that ... See full document

... other models that can explain and fit real data better than linear ...series models are proposed (namely the ExpAR-ARCH and the ExpAR-GARCH), which are combinations of a nonlinear model in the ... See full document

... the volatility without the assumption that variances and the error terms will be constant over ...of ARCH, called GARCH, which stands for Generalized Autoregressive Conditional Heteroscedasticity is ... See full document

... that ARCH models are not often used in financial markets because the simple GARCH models perform so much ...the ARCH (p) process, Bollerslev (1986) develops GARCH (p,q) ... See full document

... the ARCH (Engle, 1982) GARCH (Bollerslev, 1986) model, ...generalized ARCH (or GARCH) models, these models have become extremely common among both academics and ...practitioners. ... See full document

... market volatility with structural break using symmetric and asymmetric GARCH ...symmetric GARCH model, the GARCH coefficient in the post- break period is higher compared to the ... See full document

... asymmetric GARCH models-GARCH (1,1), PARCH (1,1), EGARCH (1,1), TARCH (1,1) and IGARCH (1,1) were used to examine stylized facts of daily USD/UGX return series from September 01, 2005 to August 30, ... See full document

... the ARCH and GARCH models are sufficient to capture all of the volatility clustering and unconditional returns distribution with heavy tails that is present from financial time series ...in ... See full document

... different GARCH-type models used in this research, namely GARCH, EGARCH, TGARCH, Asymmetric Power ARCH (APARCH), Component GARCH (CGARCH) and Asymmetric Component GARCH ... See full document

... the ARCH and GARCH ...the ARCH and GARCH models results in issue of conver- gence as some of the diagonals of the matrix of these parameters are ...these models are most often ... See full document

... data; volatility changes over time; distribution of the data is heavy-tailed, asymmetric and therefore not ...The ARCH (Autoregressive Conditionally Heteroscedastic) model was introduced by Engle in 1982 ... See full document

... Bollerslev (1986) proposes a useful extension known as the generalized ARCH (GARCH) model. For a log return series r t , we assume that the mean equation of the process can be adequatedly described by an ... See full document

... of GARCH model under three alternative distributions (normal, Student-t and skewed ...both ARCH and GARCH coefficients are positive for all our time series ...a GARCH (1, 1) the ... See full document

... of volatility as a result of non-constant variance, unexpected events and uncertainty in ...years, volatility in time series has become an important aspect in many financial ...of volatility ... See full document

... the GARCH model outperformed the ARCH ...the GARCH model to examine patterns of volatility in the US forex market and results were generally ...of ARCH and GARCH, several ... See full document

... high volatility due to the influence of multiplicity of variables operating in the economic system of a country and in the world ...This volatility in turn influences the activities of different ... See full document

... of volatility of stock price returns, a good number of researchers have become involved in modeling and making comparisons of which model is good in forecasting the stock price ...that GARCH performs better ... See full document

... the ARCH-LM test rejects the no ARCH effect hypothesis, thus indicating the presence of volatility clustering, long memory and a GARCH-type specification should be considered in the ... See full document