Results 181 to 190 of about 2,269 (199)
Some of the next articles are maybe not open access.
Use of FIGARCH models in Expected Shortfall
2017Στα 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
Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations
Journal of International Money and Finance, 2002Abstract 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.
Beine, Michel +2 more
openaire +2 more sources
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
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, 2010In 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
Measuring persistence in stock market volatility using the FIGARCH approach
Physica A: Statistical Mechanics and its Applications, 2014Abstract This paper examines the long memory property in the conditional variance of the G7’s major stock market indices, using the FIGARCH model. The GARCH and IGARCH frameworks are also estimated for comparative purposes. To this end, a dataset encompassing the daily returns of the S&P/TSX 60, CAC 40, DAX 30, MIB 30, NIKKEI 225, FTSE 100 and S&P ...
openaire +2 more sources
Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model
Journal of Empirical Finance, 2009Abstract 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, 2009The 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
Development of out-of-sample forecast formulae for the FIGARCH model
Model Assisted Statistics and ApplicationsVolatility is a matter of concern for time series modeling. It provides valuable insights into the fluctuation and stability of concerning variables over time. Volatility patterns in historical data can provide valuable information for predicting future behaviour. Nonlinear time series models such as the autoregressive conditional heteroscedastic (ARCH)
Rakshit, Debopam, Paul, Ranjit Kumar
openaire +1 more source
Long memory and FIGARCH models for daily and high frequency commodity prices [PDF]
, 2007 Daily futures returns on six important commodities are found to be well described as FIGARCH fractionally integrated volatility processes, with small departures from the martingale in mean property. The paper also analyzes several years of high frequency intra day commodity futures returns and finds very similar long memory in volatility features at ...
Richard T. Baillie +3 more
openaire +1 more source
Long memory behavior in the returns of Pakistan stock market: Arfima-Figarch models [PDF]
International Journal of Economics and Financial Issues, 2014 This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student-t and GED distribution.
TURKYILMAZ, Serpil, BALIBEY, Mesut
openaire +2 more sources