Dependent Metaverse Risk Forecasts with Heteroskedastic Models and Ensemble Learning
Risks, 2023 Metaverses have been evolving following the popularity of blockchain technology. They build their own cryptocurrencies for transactions inside their platforms.
Khreshna Syuhada +2 more
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Spatial Autoregressive Conditional Heteroskedasticity Models
JOURNAL OF THE JAPAN STATISTICAL SOCIETY, 2017 Summary: This study proposes a spatial extension of time series autoregressive conditional heteroskedasticity (ARCH) models to those for areal data. We call the spatially extended ARCH models as spatial ARCH (S-ARCH) models. S-ARCH models specify conditional variances given surrounding observations, which constitutes a good contrast with time series ...
Sato, Takaki, Matsuda, Yasumasa
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Examination of Weekend Effect and Caparison of Individual and Legal Investor's Behavior During 1381-85 in Tehran Stock Exchange [PDF]
تحقیقات مالی, 2010 In this article using Autoregressive (AR), Autoregressive conditional heteroskedasticity (ARCH), Generalized autoregressive conditional heteroskedasticity (GARCH) Models we assess the weekend effect and also compare the trading patterns of individual and
Gholam Reza Eslami Bidgoli +1 more
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Dynamic Volatility Modeling of Indonesian Insurance Company Stocks
Jurnal Ekonomi dan Studi Pembangunan, 2022 The Indonesian capital market is one of the investment destination countries for investors in developed countries. The development of economic conditions in Indonesia itself is considered suitable for investors to invest.
Budiandru Budiandru
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ARCHModels.jl: Estimating ARCH Models in Julia
Journal of Statistical Software, 2023 This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models.
Simon A. Broda, Marc S. Paolella
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Forecasting gains by using extreme value theory with realised GARCH filter
IIMB Management Review, 2021 Early empirical evidence suggests that the realised generalised autoregressive conditional heteroskedasticity (GARCH) model provides significant forecasting gains over the standard GARCH models in volatility forecasting.
Samit Paul, Prateek Sharma
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Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes [PDF]
Nonlinear Processes in Geophysics, 2005 Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average ...
W. Wang +4 more
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Inference in VARs with conditional heteroskedasticity of unknown form [PDF]
Journal of Econometrics, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brüggemann, Ralf +2 more
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Bayesian Inference for the Mixed Conditional Heteroskedasticity Model [PDF]
SSRN Electronic Journal, 2005 Summary: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of \textit{M. Haas} et al. [Mixed normal conditional heteroskedasticity. J. Financial Econ. 2, 211--250 (2004)]. We construct a Gibbs sampler algorithm to compute posterior and predictive densities.
Luc Bauwens, Jeroen V.K. Rombouts
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The Impact of Weather Factors on Quotations of Energy Sector Companies on Warsaw Stock Exchange
Energies, 2021 Recent researches on behavioral finance have tested for, among others, evidence for the relations between weather, investors’ mood, and investment decisions.
Waldemar Tarczyński +4 more
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