Results 181 to 190 of about 2,339 (211)

Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations

Journal of International Money and Finance, 2002
Abstract In this paper, we investigate the effects of official interventions on the (short run) evolution and volatility of exchange rates. To this aim, we rely on a new measure of volatility implied by the FIGARCH model that outperforms the traditionally used GARCH one.
Michel Beine, Agnes Benassy-Quere, Christelle Lecourt   +2 more
exaly   +3 more sources

An improved FIGARCH model with the fractional differencing operator (1-νL)

Finance Research Letters, 2023
Qunxing Pan, Peng Li, Xiuli Du
exaly   +2 more sources

Volatility persistence in metal returns: A FIGARCH approach

Journal of Economics and Business, 2012
Abstract This study examines the returns and the long-memory properties of the return volatilities of four metals – copper, gold, platinum, and silver. Daily returns for the January 4, 1999 to March 10, 2009 period are used. Three key issues are addressed: (1) whether the volatility processes exhibit long-run temporal dependence; (2) whether the ...
Steven J. Cochran, Iqbal Mansur, Babatunde Odusami   +2 more
openaire   +1 more source

Great Salt Lake Surface Level Forecasting Using FIGARCH Model

Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C, 2007
In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity ...
Qianru Li, Christophe Tricaud, Rongtao Sun, YangQuan Chen   +3 more
openaire   +1 more source

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.
P. Bagavathi Sivakumar, V. P. Mohandas
openaire   +1 more source

Use of FIGARCH models in Expected Shortfall

2017
Στα οικονομικά, ένα από τους βασικούς στόχους είναι η εκτίμηση της μεταβλητότητας, από τη στιγμή που παίζει σημαντικό ρόλο στην ανάλυση και στη διαχείριση του κινδύνου. Για αυτό το λόγο, έχουν αναπτυχθεί σύγχρονες ποσοτικές μέθοδοι, οι οποίες χρησιμοποιούν γνώσεις από την οικονομία, την στατιστική και τον προγραμματισμό για να πετύχουν το στόχο τους ...
openaire   +1 more source

Multivariate Diagonal FIGARCH: Specification, Estimation and Application to Modelling Exchange Rates Volatility

SSRN Electronic Journal, 2001
This paper extends the FIGARCH long-memory volatility model to a multivariate framework. The proposed quasi maximum likelihood estimator for the parameters of the model is analyzed through Monte Carlo simulations and is found to perform satisfactorily. A trivariate specification is applied for modelling jointly the daily volatility of foreign exchange ...
Pafka, S, Mátyás, László
openaire   +2 more sources

FIGARCH model on Chinese securities market based on the genetic algorithms

2010 3rd International Congress on Image and Signal Processing, 2010
In this paper, a new method of Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedasticity (FIGARCH) model for characterizing financial market volatility is introduced to test the long memory property. We also introduce a new method to establish FIGARCH model — Genetic Algorithms (GA).
Yong Lin, Lei Wu
openaire   +1 more source

Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model

Journal of Empirical Finance, 2009
Abstract This paper introduces the Smooth Transition version of FIGARCH model which is designed to account for both long memory and nonlinear dynamics in the conditional variance. Nonlinearity is introduced via a logistic transition function. The model can capture smooth changes in the volatility across different regimes as well as asymmetric ...
openaire   +1 more source

Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models

Computational Statistics, 2009
The tail index \(\alpha\) and long memory parameter \(d\) are estimated for stationary linear ARCH (LARCH) and fractionally integrated GARCH (FIGARCH) processes with heavy tailed marginal distributions and long memory. The estimates are based on the discrete wavelet transform (DWT). A confidence interval for \(\alpha\) is constructed.
Jach, Agnieszka, Kokoszka, Piotr
openaire   +2 more sources