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Modeling long-range dependence in European time-varying term premia
Sandrine Lardic,Valérie Mignon +1 more
TLDR
In this article, the authors test whether long-term dependent processes are appropriated for modeling European term premia volatility series, through the estimation of FIGARCH in mean processes, and show that the longterm component of volatility has an impact on term premias series.Abstract:
our object is to test whether long-term dependent processes are appropriated for modeling European term premia volatility series. Through the estimation of FIGARCH in mean processes, we show that the long-term component of volatility has an impact on term premia series. JEL Classification: C22, E43.read more
References
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Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Regression and time series model selection in small samples
TL;DR: In this article, a bias correction to the Akaike information criterion, called AICC, is derived for regression and autoregressive time series models, which is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample sample size.
Journal ArticleDOI
The estimation and application of long memory time series models
John Geweke,Susan Porter-Hudak +1 more
TL;DR: In this article, a new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic regressor.
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Estimation of Time Varying Risk Premia in the Term Structure: the ARCH-M Model
TL;DR: In this paper, an extension of the ARCH model was proposed to allow the conditional variance to be a determinant of the mean and is called ARCH-M. The model explains and interprets the recent econometric failures of the expectations hypothesis of the term structure.
Journal ArticleDOI
Fractionally integrated generalized autoregressive conditional heteroskedasticity
TL;DR: In this article, the FIGARCH (Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic) process is introduced and the conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations.
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