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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Functional generalized autoregressive conditional heteroskedasticity [PDF]
Journal of Time Series Analysis, 2015 Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of ARCH and GARCH processes. More recently multivariate variants of these processes have been in the focus of research
Aue, Alexander +2 more
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Modelling time-varying volatility using GARCH models: evidence from the Indian stock market [version 2; peer review: 2 approved] [PDF]
F1000Research, 2022 Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts.
Tarunpreet Kaur +3 more
doaj +2 more sources
Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form [PDF]
SSRN Electronic Journal, 2002 Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d.
Gonçalves, Sílvia, Kilian, Lutz
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Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity [PDF]
, 2003 Advanced information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, 2003. Empirical research in macroeconomics as well as in financial economics is largely based on time series. Ever since Economics Laureate Trygve Haavelmo's
Committee, Nobel Prize
core +1 more source
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.
Silvennoinen, Annastiina +1 more
core +3 more sources
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
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SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS WITH AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY
Econometric Theory, 2023 In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in the 1980s, and it means that the transition probability measures converge to the ...
Mika Meitz, Pentti Saikkonen
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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
doaj +1 more source
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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