Results 151 to 160 of about 467 (180)

Modeling and predicting stock returns using the ARFIMA-FIGARCH

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models.
Palaniappan Bagavathi Sivakumar, V. P. Mohandas   +1 more
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ARFIMA processes and outliers: a weighted likelihood approach

Journal of Applied Statistics, 2010
In this paper, we consider the problem of robust estimation of the fractional parameter, d,  in long memory autoregressive fractionally integrated moving average processes, when two types of outliers, i.e. additive and innovation, are taken into account without knowing their number, position or intensity.
AGOSTINELLI C, BISAGLIA, LUISA
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Modeling of PMU Data Using ARFIMA Models

2018 Clemson University Power Systems Conference (PSC), 2018
Installing Phasor Measurement Units (PMUs) in the smart grid has played an important role in having more reliable and secure grid. Due to the high sampling rate (50 samples/s), PMU generates massive amount of data compared to the conventional SCADA system.
Laith Shalalfeh, Paul Bogdan, Edmond Jonckheere   +2 more
openaire   +1 more source

On the Spectral Density of the Modified-ARFIMA Model

Journal of Chartered Institute of Statisticians of Nigeria
This study develops the Modified-ARFIMA Model and its spectral density for a recursive sequence differencing operator that can handle large data in time series that have long memory characteristics.
A. Bello, M. Tasi’u, H. G. Dikko, B. B. Alhaji   +3 more
openaire   +1 more source

Calculating and analyzing impulse responses for the vector ARFIMA model

Economics Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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ARFIMA modely časových řad

2014
The thesis deal with long-memory processes which are defined by several ways. The main concern is dedicated to ARFIMA model, to its basic properties and its application. Next, graphical, semiparametric and parametric estimation methods of ARFIMA parameters are described in detail.
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Adaptive ARFIMA Models of Inflation

SSRN Electronic Journal, 2011
Claudio Morana, Richard Baillie
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Spectral Methods for Fractional Arfima Processes

2002
Nel presente lavoro, nella finalità di stimare il parametro di lunga memoria di un processo ARIMA frazionale, ci si avvale di metodologie basate sulla decomposizione ortogonale del processo temporale. In particolare si propone un’integrazione dell’analisi di Fourier con l’espansione di Karhunen-Loève e con la trasformata discreta wavelet al fine di ...
COLI, Mauro, FONTANELLA, Lara, GRANTURCO M.   +2 more
openaire   +1 more source

Time-varying ARFIMA-GARCH model with symmetric thresholds: applications to inflation

Applied Economics Letters, 2021
Juan Liu, Zhengxun Tan
exaly  

Another look at the forecast performance of ARFIMA models

International Review of Financial Analysis, 2004
Craig Ellis
exaly