Results 41 to 50 of about 2,061 (206)
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
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Statistical modelling for forecasting volatility in potato prices using ARFIMA-FIGARCH model
The Indian Journal of Agricultural Sciences, 2018 This 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 generalized autoregressive conditional heteroscedastic (FIGARCH) models.
DIPANKAR MITRA +2 more
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Modeling long-term volatility memory dynamics in the Colombo Stock Exchange [PDF]
IIM Ranchi Journal of Management StudiesPurposeThis study examines the long-term volatility memory dynamics of the Colombo Stock Exchange by comparing the behaviors of the All Share Price Index (ASPI) and the S&P SL20 Index under recent economic scenarios.Design/methodology/approachThe paper ...
Mohamed Ismail Mohamed Riyath
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Fractional derivatives of random walks: Time series with long-time memory
, 2008 We review statistical properties of models generated by the application of a (positive and negative order) fractional derivative operator to a standard random walk and show that the resulting stochastic walks display slowly-decaying autocorrelation ...
G. Samorodnitsky +10 more
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A Study of Nigeria Monthly Stock Price Index Using ARTFIMA-FIGARCH Hybrid Model
UMYU Scientifica, 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 forecasting reliability. Meanwhile, the Autoregressive Tempered Fractional Integrated Moving Average (ARTFIMA)
A G Umar, H G Dikko, J Garba, M Tasi’u
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East Asian Economic Review, 2016 This paper quantitatively compares the intrinsic features of the daily USD-GBP exchange rates in two different periods, the 1920s and the 2010s, under the same freely floating exchange rate system.
Young Wook Han
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Journal of Futures Markets, Volume 45, Issue 8, Page 917-945, August 2025.ABSTRACT This paper investigates the economic consequences for Bitcoin options' prices of a long memory in conditional volatility and conditional non‐normality of Bitcoin returns. The arbitrage‐free prices of Bitcoin options are determined by market consistent valuation and the conditional Esscher transform. Monte Carlo estimates for option prices from
Tak Kuen Siu
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Spatial and spatiotemporal volatility models: A review
Journal of Economic Surveys, Volume 39, Issue 3, Page 1037-1091, July 2025.Abstract Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, in the case of positive spatial dependence, if two locations are in close proximity ...
Philipp Otto +4 more
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Discrete Dynamics in Nature and Society, Volume 2018, Issue 1, 2018., 2018 Memory in finance is the foundation of a well‐established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V‐statistic on the basis of a modified R/S
Peng Zheng +3 more
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Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange [PDF]
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī, 2014 This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX ...
Mohammad Javad Mohagheghnia +3 more
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