12 research outputs found
A long memory model with mixed normal GARCH for US inflation data
We introduce a time series model that captures both long
memory and conditional heteroskedasticity and assess its ability to
describe the US inflation data. Specifically, the model allows for
long memory in the conditional mean formulation and uses a
normal mixture GARCH process to characterize conditional
heteroskedasticity. We find that the proposed model yields a good
description of the salient features, including skewness and
heteroskedasticity, of the US inflation data. Further, the performance
of the proposed model compares quite favorably with, for example,
ARMA and ARFIMA models with GARCH errors characterized by
normal, symmetric and skewed Student-t distributions
A Long Memory Model with Mixed Normal GARCH for US Inflation Data
We introduce a time series model that captures both long memory and conditional heteroskedasticity and assess their ability to describe the US inflation data. Specifically, the model allows for long memory in the conditional mean formulation and uses a normal mixture GARCH process to characterize conditional heteroskedasticity. We find that the proposed model yields a good description of the salient features, including skewness and heteroskedasticity, of the US inflation data. Further, the performance of the proposed model compares quite favorably with, for example, ARMA and ARFIMA models with GARCH errors characterized by normal, symmetric and skewed Student-t distributions
Asymptotics of trend stationary fractionally integrated ARMA models
The main contribution of this study is to provide asymptotic results for MLE applied to the trend stationary ARFIMA model and to implement a detailed simulation study. For small sample sizes, the bias for the fractional parameter, d, can be quite substantial when the other parameters are also estimated simultaneously.
Bivariate mixed normal GARCH models and out-of-sample hedge performances
This study compares bivariate mixed normal GARCH models with standard bivariate GARCH models in terms of the percentage variance reduction of the out-of-sample hedged portfolio and also statistical significance tests of performance improvements using Superior Predictive Ability statistics. All competing models are applied to corn and wheat futures and empirical results demonstrate that the standard BEKK-GARCH model significantly outperforms the other competing GARCH models at shorter horizons. However, as the hedge horizon is extended to longer than 10รย days, it is evident that the mixed normal BEKK-GARCH model is the best at the usual significance level of 5%.Hedge performances Regime-dependent correlations Conditional variance Bivariate mixed normal BEKK-GARCH SPA tests
A Long Memory Model with Mixed Normal GARCH for US Inflation Data
We introduce a time series model that captures both long memory and conditional heteroskedasticity and assess their ability to describe the US inflation data. Specifically, the model allows for long memory in the conditional mean formulation and uses a normal mixture GARCH process to characterize conditional heteroskedasticity. We find that the proposed model yields a good description of the salient features, including skewness and heteroskedasticity, of the US inflation data. Further, the performance of the proposed model compares quite favorably with, for example, ARMA and ARFIMA models with GARCH errors characterized by normal, symmetric and skewed Student-t distributions
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A Long Memory Model with Mixed Normal GARCH for US Inflation Data
We introduce a time series model that captures both long memory and conditional heteroskedasticity and assess their ability to describe the US inflation data. Specifically, the model allows for long memory in the conditional mean formulation and uses a normal mixture GARCH process to characterize conditional heteroskedasticity. We find that the proposed model yields a good description of the salient features, including skewness and heteroskedasticity, of the US inflation data. Further, the performance of the proposed model compares quite favorably with, for example, ARMA and ARFIMA models with GARCH errors characterized by normal, symmetric and skewed Student-t distributions
Modeling and forecasting from trend-stationary long memory models with applications to climatology
A Long Memory Model with Normal Mixture GARCH
Long memory, Normal mixture, Inflation rate, Conditional heteroskedasticity,