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Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models [PDF]
Proceedings of Machine Learning Research, Volume 51, 2016, pp.
296-304, 2018 A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance.
Balázs Csanád Csáji
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Spatial extension of generalized autoregressive conditional heteroskedasticity models [PDF]
Spatial Economic Analysis, 2020 This paper proposes an extension of generalized autoregressive conditional heteroskedasticity (GARCH) models for a time series to those for spatial data, which are called here spatial GARCH (S-GARCH) models. S-GARCH models are re-expressed as spatial autoregressive moving-average (SARMA) models and a two-step procedure based on quasi-likelihood ...
Takaki Sato, Yasumasa Matsuda
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Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling [PDF]
International Journal of Statistics and Economics, 19(3), 2018, 2019 This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the log returns was modeled using GARCH.
Novy Ann M. Etac, Roel F. Ceballos
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Maximum likelihood estimation of a noninvertible ARMA model with autoregressive conditional heteroskedasticity [PDF]
Journal of Multivariate Analysis, 2012 We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to the so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity.
Mika Meitz, Pentti Saikkonen
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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. These procedures are invalid in the presence of conditional heteroskedasticity.
Sílvia Gonçalves +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,
Tim Bollerslev +2 more
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Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model [PDF]
Journal of Econometrics, 2021 This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood ...
Feiyu Jiang, Dong Li, Ke Zhu
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Long‐run predictability tests are even worse than you thought
Journal of Applied Econometrics, Volume 37, Issue 7, Page 1334-1355, November/December 2022., 2022 Summary We derive asymptotic results for the long‐horizon ordinary least squares (OLS) estimator and corresponding t$$ t $$‐statistic for stationary autoregressive predictors. The t$$ t $$‐statistic—formed using the correct asymptotic variance—together with standard‐normal critical values result in a correctly‐sized test for exogenous predictors.
Erik Hjalmarsson, Tamas Kiss
wiley +1 more source
Functional generalized autoregressive conditional heteroskedasticity [PDF]
arXiv, 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 with attention given to methods seeking an efficient and economic estimation of a large number of ...
Alexander Aue +2 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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