Results 31 to 40 of about 6,621,438 (152)
The Indian Journal of Agricultural Sciences, 2022 The objective of present study was to investigate the efficiency of Autoregressive fractionally integrated moving average model with exogenous input (ARFIMAX) in forecasting price of Indian mustard [Brassica juncea (L.) Czern. & Coss].
RANJIT KUMAR PAUL +4 more
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On Short‐Term Behavior of Implied Volatility for Index Options
Mathematical Finance, EarlyView.ABSTRACT This paper investigates short‐term behavior of implied volatility of derivatives written on a market index when the index is constructed using a ranking procedure. Even when stock prices follow geometric Brownian motion dynamics, the ranking mechanism can lead to the observed term structure of at‐the‐money (ATM) implied volatility skew for ...
Huy N. Chau, Duy Nguyen, Thai Nguyen
wiley +1 more source
Lecturas de Economía, 2011 Sesgos en estimación, tamaño y potencia de una prueba sobre el parámetro de memoria larga en modelos ARFIMA Resumen: Castaño et al. (2008) proponen una prueba para investigar la existencia de memoria larga, basada en el parámetro de diferenciación ...
Elkin Castaño Vélez +2 more
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Applied Research, Volume 5, Issue 5, October 2026.Through a PRISMA‐guided review of 42 studies, this work compares deep learning, ensemble machine learning, and statistical/econometric models for long‐term energy demand forecasting, showing that model choice should balance accuracy, interpretability, data availability, and policy relevance.
Nor Afiza Mohd Noor +4 more
wiley +1 more source
Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting
IEEE Access, 2020 Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their ...
Ayaz Hussain Bukhari +5 more
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GCB Bioenergy, Volume 18, Issue 10, October 2026.Explainable AI reveals how biological feedstock quality, energy markets, and policy factors jointly shape ethanol price dynamics. Deep learning improves forecasting accuracy while identifying sugarcane quality (TRS), gasoline prices, taxation, and macroeconomic variables as key drivers in a major bioenergy system.
Marilaine Colnago +4 more
wiley +1 more source
FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS
Malaysian Journal of Computing, 2018 Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product ...
P.J.W. Mah, N.N.M. Zali, N.A.M. Ihwal, N.Z. Azizan
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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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Australian Economic Papers, Volume 65, Issue 3, Page 222-260, September 2026.ABSTRACT This study examines volatility interconnectedness among selected agricultural commodities and precious/industrial metals, together with oil price uncertainty and global supply chain pressure, over the period January 1998 to June 2024 using a Quantile‐on‐Quantile connectedness framework.
Muhammed Benli, Halil Altıntaş
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Physical Review Research, 2022 We introduce a method for reconstructing macroscopic models of one-dimensional stochastic processes with long-range correlations from sparsely sampled time series by combining fractional calculus and discrete-time Langevin equations.
Johannes A. Kassel, Holger Kantz
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