Results 41 to 50 of about 13,793,172 (210)
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
A hybrid ARFIMA wavelet artificial neural network model for DJIA index forecasting
, 2023 This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete ...
Canarella, Giorgio +3 more
core +1 more source
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
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
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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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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
doaj +1 more source
Wavelet based long memory model for modelling wheat price in India
The Indian Journal of Agricultural Sciences, 2021 Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year.
RANJIT KUMAR PAUL +2 more
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Journal of Forecasting, Volume 45, Issue 4, Page 1579-1600, July 2026.ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann +2 more
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Jurnal LebesgueThe Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is a development of the ARIMA model with the differencing values being fractional numbers.
Muhammad Reja Sinaga +2 more
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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
doaj +1 more source