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, 2018
Not AvailableThis paper investigates the presence of long memory both in mean and volatility in the potato prices in Agra and Amritsar markets of India, using the Autoregressive fractionally integrated moving average (ARFIMA) and Fractionally integrated ...
Amrit Kumar Paul, Ranjit Kumar Paul, Dipankar Mitra   +2 more
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Forecasting value-at-risk of crude oil futures using a hybrid ARIMA-SVR-POT model. [PDF]

Heliyon
Zhang C, Zhou X.
europepmc   +1 more source

One-day-ahead value-at-risk estimations with dual long-memory models: evidence from the Tunisian stock market

In this paper, we assess the one-day-ahead Value-at-Risk (VaR) performance for the Tunisian Stock Market (TSE). Using the ARFIMA-FIGARCH and ARFIMA-FIAPARCH models under three alternative innovation distributions: normal, Student and skewed Student, we ...
Samir Mabrouk, Chaker Aloui
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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
exaly   +3 more sources

Testing for long memory in ISE using Arfima-figarch model and structural break test [PDF]

, 2009
This study examines long memory in Istanbul Stock Exchange (ISE) by using the structural break test in variance and ARFIMA-FIGARCH model. Our findings indicate that long memory does not exist in the equity return; however, it exits in volatility. Consequently, ISE is found as a weak form inefficient market due to volatility as it has a predictable ...
Korkmaz, Turhan, Çevik, Emrah Ismail, Özataç, Nesrin   +2 more
core   +5 more sources

Arfima-Figarch vs. Arfima-Hygarch: Case Study ETF Returns of Emerging Asian Countries

Asian Journal of Finance and Accounting, 2014
This research investigate the long memory returns for ETF returns index of seven Asian countries in Emerging Markets Equities during 2008-2013 periods. Those ETFs are Wisdom Tree Indian Rupee Fund (ICN), Market Vectors Indonesia Index (IDX), iShares MSCI Malaysia Index Fund (EWM), Market Vectors Russia ETF (RSX), and iShares MSCI Thailand Investable ...
exaly   +2 more sources

Comparing the Intensity of Long-Memory Symmetry in the Returns of the Capital Market Index and Bahar Azadi Coin: An ARFIMA–FIGARCH Approach

Dynamic Management in Business Analysis
Objective: The study aims to compare the structure of long memory in the returns and volatility of the Tehran Stock Exchange Price Index (TEPIX) and the Bahar Azadi gold coin. Methodology: Using daily data from December 2008 to September 2025, the study employs the ARFIMA model to analyze long memory in returns and the FIGARCH model to assess long ...
Mojtaba Abolhasani Pourashkzar, Ahmad Sarlak, Teymor Mohammadi, Gholamali Haji   +3 more
exaly   +2 more sources

Forecasting crude palm oil (CPO) prices with Arfima-Figarch method

AIP Conference Proceedings
Maulidya Maghfiro, Trianingsih Eni Lestari   +1 more
exaly   +2 more sources

Did the Returns and Volatilities in BRIC Countries Follow Long Memory During Global Crisis? Did the Reaction Time Vary Across BRIC Countries During Global Crisis? Evidence from ARFIMA-FIGARCH

SSRN Electronic Journal, 2011
The purpose of this paper is to investigate the effects of financial crisis on the trading behavior of BRIC stock markets. The paper employs the ARFIMA-FIGARCH framework to examine the long memory in equity returns and volatilities and to assess the predictable nature of the financial time series data during the global financial crisis.
Sabur Mollah, Amir Kheirollah, Shahiduzzaman Quoreshi   +2 more
openaire   +1 more source

Comparative Approach to the Backward Elimination and for-ward Selection Methods in Modeling the Systematic Risk Based on the ARFIMA-FIGARCH Model

2017
The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model.
Rastgoo, Nemat, Panahian, Hossein
openaire   +1 more source