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Showing 1 to 11 of 11 for “"GARCH-type models"”.
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Application of GARCH Type Models in Forecasting Value at Risk
… using four conditional volatility forecasting models: GARCH, TGARCH, GJRGARCH, and IGARCH, and compares the forecasting output of the suggested GARCH-based volatility models. Since the predictive accuracy of Value-at-Risk (VaR) models is crucial for adequate capitalization, we perform …
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Essays in volatility modelling
… two recently proposed (discrete time) volatility models. Subsequently, we propose a new model that allows for conditional heteroskedasticity in the volatility of asset returns and incorporates current return information into the volatility nowcast and forecast. Our model can capture most stylised …
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Volatility Modeling Using the Student's t Distribution
… produced a wealth of univariate and multivariate GARCH type models. While the univariate models have been relatively successful in empirical studies, they suffer from a number ofweaknesses, such as unverifiable parameter restrictions, existence of moment conditions and the retention of Normality. …
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Multiproduct Nonconstant and Time -Varying Hedge Ratio Estimation by Locally Polynomial Kernel Applied to the Hog Complex
… of more information inflow on prices than GARCH-type models. Estimated hedge ratios using past information might not be relevant to hog producers to manage their price risks in reality. Thus, hedge ratios are one-step ahead forecasted, ex ante hedge ratios, and evaluated for an …
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Oil Price Movements and Equity Returns: Evidence from the GCC Countries
… the equity returns volatility requires using GARCH-type models. These models help to explore the pronounced differences of the conditional variance structures across sectors and markets. Chapter 1 compares the effects of changes in oil price return and its volatility on equity returns and …
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Vector Generalized Linear Time Series Models with an Implementation in R
… class for multivariate TS and the ARCH-GARCH-type models for heteroskedasticity. The result has been an explosion of TS models and inference schemes having pockets of substructure but limited overriding framework. In this work, the class of vector generalized linear models (VGLMs) is …
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Stochastic modelling in Financial markets: case study of the Nigerian Stock Market
This research uses suitable stochastic models typically encountered in empirical and quantitative financial economics to analyse stock market data from the Nigerian Stock Market (NSM), in light of a) possible changes in the policy environments as result of the 2004 financial reforms by the then …
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Essays on Conditional Heteroscedastic Time Series Models with Asymmetry, Long memory, and Structural Changes
… time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type models, are further generalized to accommodate …
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Contributions to Conditional Heteroscedastic Models: M-Estimation and Other Methods.
… contributions to conditional heteroscedastic models in financial time series. A class of M-estimators for time series models with asymmetric form of heteroscedasticity are developed. A weighted resampling method is used to approximate the sampling distribution of M-estimators. The primary …
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Essays On Robust Estimators For Non-Identically Distributed Observations In Spatial Econometric And Time Series Models
… methods and applications of spatial econometric models and one essay on the generalized autoregressive conditionally heteroskedastic (GARCH)-type models in financial time series. The first essay discusses the heteroskedasticity robust generalized method of moments estimator (RGMME) for the …
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Essays in Financial Econometrics and Forecasting
… the observation that most parametric volatility models follow Engle's (1982) original idea of modelling the volatility of asset returns as a function of only past information. However, current returns are potentially quite informative for forecasting, yet are excluded from these models. The first …