Spatio-Temporal Generalized Autoregressive Conditional Heteroskedasticity Models
, 2016 This thesis presents a spatio-temporal extension of the GARCH process with a specific spatial dependence structure. Different simulation and estimation techniques are developed. Assuming a circular spatial structure at each time point, gives a closed and finite set of variables at each point in time, making the spatio-temporal process adapted in the ...
Sondre Hølleland
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Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations [PDF]
, 2005 In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into correlations and standard deviations. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to an ...
Annastiina Silvennoinen +1 more
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Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models
Data Science in Finance and Economics, 2023 The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk.
Moses Khumalo +2 more
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Periodic Autoregressive Conditional Heteroscedasticity [PDF]
Journal of Business & Economic Statistics, 1996 Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH,
Bollerslev, T, Ghysels, E
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A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model
Axioms, 2022 Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the
Sang-Ha Sung +3 more
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Short-term user load forecasting based on GARCH-M family model with different distributions
电力工程技术, 2022 Power load forecasting is one of the basic tasks power system research,and time series analysis is currently the most widely used forecasting method. Aiming at the fluctuation and the characteristics of peak and thick tail of user daily load time series ...
WANG Chen, YE Jiangming, HE Jiahong
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Probabilistic Forecasting of Wind Power Generation Using Online LASSO VAR and EGARCH Model
Shanghai Jiaotong Daxue xuebao, 2023 Wind power generation has uncertainty due to the high fluctuation of wind speed. In traditional wind power prediction models, the uncertainty is measured by normal distribution with zero mean and constant variance.
WANG Peng, LI Yanting, ZHANG Yu
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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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Mildly Explosive Autoregression Under Stationary Conditional Heteroskedasticity [PDF]
Journal of Time Series Analysis, 2018 A limit theory is developed for mildly explosive autoregressions under stationary (weakly or strongly dependent) conditionally heteroskedastic errors. The conditional variance process is allowed to be stationary, integrable and mixingale, thus encompassing general classes of generalized autoregressive conditional heteroskedasticity‐type or stochastic ...
Arvanitis, Stelios, Magdalinos, Tassos
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Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form [PDF]
SSRN Electronic Journal, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
GONÇALVES, Silvia, KILIAN, Lutz
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