Results 21 to 30 of about 2,588 (267)
Health and Quality of Life Outcomes, 2022 Background Respondents in a health valuation study may have different sources of error (i.e., heteroskedasticity), tastes (differences in the relative effects of each attribute level), and scales (differences in the absolute effects of all attributes ...
Suzana Karim +2 more
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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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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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Conditional Heteroskedasticity in Crypto-Asset Returns [PDF]
SSRN Electronic Journal, 2017 This paper examines the time series properties of cryptocurrency assets, such as Bitcoin, using established econometric inference techniques, namely models of the GARCH family. The contribution of this study is twofold. I explore the time series properties of cryptocurrencies, a new type of financial asset on which there appears to be little or no ...
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Exploring the Dynamic Links between GCC Sukuk and Commodity Market Volatility
International Journal of Financial Studies, 2018 This study investigates the impact of commodity price volatility (including soft commodities, precious metals, industrial metals, and energy) on the dynamics of corporate sukuk returns.
Nader Naifar
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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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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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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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Mixed Normal Conditional Heteroskedasticity [PDF]
Journal of Financial Econometrics, 2004 Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed -normal distribution coupled with a GARCH -type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components ...
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