Results 11 to 20 of about 291,653 (252)
Neural Generalised AutoRegressive Conditional Heteroskedasticity [PDF]
arXiv, 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
arxiv +5 more sources
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes [PDF]
arXiv, 2016 Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations ${u_t}$ = ${z_t}$ $(1-\sum\limits_{j=1}^q _j L^j) _{t}^2 = +(1-\sum\limits_{j=1}^q _j L^j - (\sum\limits_{k=1}^p _k L^k)
Adil Yilmaz, Gazanfer Ünal
arxiv +5 more sources
Generalized Autoregressive Conditional Heteroskedasticity [PDF]
Journal of Econometrics, 1995 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
+5 more sources
Functional Generalized Autoregressive Conditional Heteroskedasticity [PDF]
Journal of Time Series Analysis, 2016 Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic (GARCH) processes.
Alexander Aue +2 more
openalex +5 more sources
Subgeometrically ergodic autoregressions with autoregressive conditional heteroskedasticity [PDF]
Econom. Theory 41 (2025) 218-248, 2022 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 1980s and it means that the transition probability measures converge to the stationary ...
Mika Meitz, Pentti Saikkonen
openalex +3 more sources
Nonlinear Models for Autoregressive Conditional Heteroskedasticity [PDF]
Research Papers in Economics, 2012 This paper contains a brief survey of nonlinear models of autoregressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discussed.
Timo Teräsvirta
openalex +5 more sources
SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS WITH AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY [PDF]
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
openalex +4 more sources
Testing Coefficients of Autoregressive Conditional Heteroskedasticity Models by Graphical Approach [PDF]
International Journal of Engineering and Manufacturing, 2012 The graphical approach is applied to the autoregressive conditional heteroskedasticity time series models. After transformation, it is shown that the coefficients of GARCH model are the conditional correlation coefficients conditioned on the other components of the time series, then a new method is proposed to test the significance of the coefficients ...
Fengjing Cai, Yuan Li
openalex +3 more sources
Option pricing under linear autoregressive dynamics, heteroskedasticity, and conditional leptokurtosis [PDF]
Journal of Empirical Finance, 2001 Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e.
Christian Hafner, Helmut Herwartz
openalex +6 more sources
Testing for reduction to random walk in autoregressive conditional heteroskedasticity models [PDF]
The Econometrics Journal, 2002 The AR-ARCH and AR-GARCH models, which allow for conditional heteroskedasticity and autoregression, reduce to random walk or white noise for some values of the parameters. We consider generalised versions of the AR-ARCH(1) and AR-GARCH(1,1) models, and, under mild assumptions, calculate the asymptotic distributions of pseudo-likelihood ratio statistics
Claudia Klüppelberg +3 more
openalex +4 more sources