Results 171 to 180 of about 772 (205)
Asymmetric volatility in asset prices: An explanation with mental framing. [PDF]
HeliyonOrmos M, Timotity D.
europepmc +1 more source
On the order of integration of monthly US ex-ante and ex-post real interest rates new evidence from over a century of data [PDF]
Menelaos Karananos, N Zeng, S.H Sekioua
core
Measuring persistence in stock market volatility using the FIGARCH approach [PDF]
Physica A: Statistical Mechanics and Its Applications, 2014 Abstract 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 ...
Sonia Bentes
exaly +4 more sources
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A novel time-varying FIGARCH model for improving volatility predictions
Physica A: Statistical Mechanics and Its Applications, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuehui Chen
exaly +2 more sources
Modeling volatility with time-varying FIGARCH models
Economic Modelling, 2011Abstract This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time.
Mustapha Belkhouja
exaly +2 more sources
Modeling and predicting stock returns using the ARFIMA-FIGARCH
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009Modeling 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
exaly +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).
Lei Wu
exaly +2 more sources
Use of FIGARCH models in Expected Shortfall [PDF]
, 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 ...
Σιούρης, Γεώργιος-Ιάσων +1 more
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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.
Christelle Lecourt +2 more
exaly +4 more sources
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 +3 more sources