Results 31 to 40 of about 2,441 (205)
İKİLİ UZUN HAFIZADA ASİMETRİ ETKİSİ: BİST BANKA ÖRNEĞİ
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2019 Çalışmanın amacı, Türk bankacılık sektör endeksiningetiri ve volatilitesinde ikili uzun hafıza özelliğini ARFIMA-FIGARCH veARFIMA-FIEGARCH modeli ile inceleyerek etkin piyasalar hipotezini testetmektir. Bu amaçla modelde veri seti olarak 2008-2017 dönemi
Harun Kaya, İsmail Çelik
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On the invertibility in periodic ARFIMA models
, 2020 The present paper, characterizes the invertibility and causality conditions of a periodic ARFIMA (PARFIMA) models. We first, discuss the conditions in the multivariate case, by considering the corresponding p-variate stationary ARFIMA models. Second, we construct the conditions using the univariate case and we deduce a new infinite autoregressive ...
Amimour, Amine, Belaide, Karima
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Forecasting energy futures volatility based on the unbiased extreme value volatility estimator
IIMB Management Review, 2017 This paper uses the opening, high, low, and closing prices of five energy futures to estimate and model volatility based on the unbiased extreme value volatility estimator (the Add RS estimator).
Dilip Kumar
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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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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
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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
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Jurnal LebesgueIndonesia, a nation in Southeast Asia, has a wealth of natural resources that could serve as the basis for future economic growth. Increased exports of natural resources are crucial for market expansion, job creation, foreign exchange gains, and economic
Putri Hazizah Rahwani +2 more
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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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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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Mathematics, 2021 The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data
Pushpa Dissanayake +3 more
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