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Journal of Computing Research and Innovation, 2021 Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other ...
Atiqa Nur Azza Mahmad Azan +2 more
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SMALL-SAMPLE LIKELIHOOD-BASED INFERENCE IN THE ARFIMA MODEL [PDF]
Econometric Theory, 2000 The autoregressive fractionally integrated moving average (ARFIMA) model has become a popular approach for analyzing time series that exhibit long-range dependence. For the Gaussian case, there have been substantial advances in the area of likelihood-based inference, including development of the asymptotic properties of the maximum likelihood ...
Offer Lieberman +2 more
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Testing for long memory in volatility in the Indian Forex market [PDF]
Ekonomski Anali, 2014 This article attempts to verify the presence of long memory in volatility in the Indian foreign exchange market using daily bilateral returns of the Indian Rupee against the US dollar from 17/02/1994 to 08/11/2013.
Kumar Anoop S.
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Comparing the bias and misspecification in ARFIMA models [PDF]
Journal of Time Series Analysis, 1997 We investigate the bias in both the short‐term and long‐term parameters for a range of autoregressive fractional integrated moving‐average (ARFIMA) models using both semi‐parametric and maximum likelihood (ML) estimation methods. The results suggest that, provided the correct model is estimated, the ML method outperforms the semi‐parametric methods in ...
Smith, Jeremy +2 more
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MODELLING FOR THE WAVELET COEFFICIENTS OF ARFIMA PROCESSES [PDF]
Journal of Time Series Analysis, 2014 AbstractWe consider a model for the discrete nonboundary wavelet coefficients of autoregressive fractionally integrated moving average (ARFIMA) processes in each scale. Because the utility of the wavelet transform for the long‐range dependent processes, which many authors have explained in semi‐parametrical literature, is approximating the transformed ...
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A Study of Nigeria Monthly Stock Price Index Using ARTFIMA-FIGARCH Hybrid Model
UMYU Scientifica Journal, 2023 Long memory is a phenomenon in time series analysis that is exhibited by a slow decay of the autocorrelation function. It has been observed that the presence of long memory in both mean and volatility can complicate model fitting and compromise ...
A G Umar, H G Dikko, J Garba, M Tasi’u
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Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2017 We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors.
Perron, Pierre, Varneskov, Rasmus T.
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Annales Mathematicae Silesianae, 2018 In this survey paper we present a systematic methodology of how to identify origins of fractional dynamics. We consider three models leading to it, namely fractional Brownian motion (FBM), fractional Lévy stable motion (FLSM) and autoregressive ...
Weron Aleksander
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FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS
Malaysian Journal of Computing, 2022 The unemployment issue is one of the most common problems faced by many countries around the world. The unemployment rates in developed countries often fluctuate throughout time.
Nur Afiqah Ismail +2 more
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Journal of Hydrology and Hydromechanics, 2014 Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting.
Szolgayová Elena +3 more
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