Journal of Finance and Data Science, 2016 In this article we evaluate the daily conditional volatility and h-step-ahead Value at Risk (VaR) forecasting power of three long memory GARCH-type models (FIGARCH, HYGARCH & FIAPARCH).
Samir Mabrouk
doaj +1 more source
Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH [PDF]
, 2015 This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock ...
De Moraes, Alex Sandro Monteiro +2 more
core +1 more source
Modeling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach [PDF]
, 2007 This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process.
Morana, Claudio, Baillie, Richard T.
core
Commonality in the LME aluminium and copper volatility processes through a Figarch lens [PDF]
We consider dynamic representation of spot and three month aluminium and copper volatilities. These are the two most important metals traded in the London Metal Exchange (LME).
Christopher L. Gilbert +1 more
core
İKİLİ UZUN HAFIZADA ASİMETRİ ETKİSİ: BİST BANKA ÖRNEĞİ
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2019 Çalışmanın amacı, Türk bankacılık sektör endeksiningetiri ve volatilitesinde ikili uzun hafıza özelliğini ARFIMA-FIGARCH veARFIMA-FIEGARCH modeli ile inceleyerek etkin piyasalar hipotezini testetmektir. Bu amaçla modelde veri seti olarak 2008-2017 dönemi
Harun Kaya, İsmail Çelik
doaj +1 more source
A hybrid ExpAR-FIGARCH-ANN model for time series forecasting
Journal of Statistical Sciences and Computational IntelligenceFinancial time series forecast is challenging due to nonlinear mean dynamics, volatility clustering, and long-memory effects. Traditional hybrid models such as Autoregressive Integrated Moving Average – Generalised Autoregressive Conditional Heteroscedasticity (ARIMA–GARCH) and Fractional Generalised Integrated Autoregressive Conditional ...
Abba Bello Muhammad +5 more
openaire +1 more source
Long term hedging of the Australian All Ordinaries Index using a bivariate error correction FIGARCH model [PDF]
This article compares the performance of bivariate error correction GARCH and FIGARCH models when estimating long term dynamic minimum variance hedge ratios (MVHRs) on the Australian All Ordinaries Index.
Jonathan Dark
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PERAMALAN DATA RUNTUN WAKTU DENGAN PEMODELAN ARFIMA-FIGARCH [PDF]
, 2009 Pemodelan Autoregressive Fractional Integrated Moving Average – Fractional Integrated Generalized Autoregressive Conditional Heteroskedastic (ARFIMA-FIGARCH) adalah salah satu model peramalan dimana data time series mempunyai memori jangka panjang dengan
Selvi Affriani, -
core
Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures [PDF]
In this paper we extend the univariate FIGARCH and FIAPARCH models to a bivariate framework. We estimate bivariate error correction FIGARCH and FIAPARCH models between the All Ordinaries Index and its SPI futures using constant correlation and diagonal ...
Jonathan Dark
core
Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach [PDF]
This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH , or A-FIGARCH , which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing
Claudio Morana, Richard T. Baillie
core +3 more sources