Recurrent conditional heteroskedasticity
Journal of Applied Econometrics, 2022 SummaryWe propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in‐sample analysis and out‐of‐sample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into
Minh-Ngoc Tran, Robert Köhn
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Generalized autoregressive conditional heteroskedasticity [PDF]
Journal of Econometrics, 1986 Abstract A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived ...
Tim Bollerslev
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A TEST FOR CONDITIONAL HETEROSKEDASTICITY IN TIME SERIES MODELS [PDF]
Journal of Time Series Analysis, 1992 Abstract. When testing for conditional heteroskedasticity and nonlinearity, the power of the test in general depends on the functional forms of conditional heteroskedasticity and nonlinearity that are allowed under the alternative hypothesis. We suggest a test for conditional heteroskedasticity and nonlinearity with the nonlinear autoregressive ...
Bera, Anil K., Higgins, M.L.
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Investigating the Impact of International Sanctions on Performance Indicators of Tehran Stock Exchange with Industries Divided From 2010 to 2020 [PDF]
مدلسازی اقتصادسنجی, 2023 In this research, the impact of the impact of the international sanctions index on the performance indices of the Tehran Stock Exchange by industries, including mass production indices, banks, insurance, automobiles, investments, basic metals, rubber ...
Hamid Reza Vaezian +3 more
doaj +1 more source
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
doaj +1 more source
Neural Generalised AutoRegressive Conditional Heteroskedasticity
CoRR, 2022 We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK 1,1 model in the multivariate case.
Zexuan Yin, Paolo Barucca
openaire +2 more sources
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
doaj +1 more source
Multivariate mixed normal conditional heteroskedasticity [PDF]
Computational Statistics & Data Analysis, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luc Bauwens +2 more
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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
doaj +1 more source
Measure the Variation in Inflation Rates in Egypt using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Models. [PDF]
The Egyptian Statistical Journal, 2019 A model of generalized autoregressive conditional heteroscedastic was best to study the variance of inflation rates in Egypt from January 2006 to December 2017.
Emad Eldin Aly
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