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Conditional Heteroskedasticity in Asset Returns: A New Approach
Econometrica, 1991Abstract GARCH models have been applied in modelling the relation between conditional variance and asset risk premia. These models, however, have at least three major drawbacks in asset pricing applications: (i) Researchers beginning with Black (1976) have found a negative correlation between current returns and future returns ...
Daniel B Nelson
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Bootstrapping Neural tests for conditional heteroskedasticity [PDF]
, 2006 We deal with bootstrapping tests for detecting conditional heteroskedasticity in the context of standard and nonstandard ARCH models. We develope parametric and nonparametric bootstrap tests based both on the LM statistic and a neural statistic. The neural tests are designed to approximate an arbitrary nonlinear form of the conditional variance by a ...
Carole Siani, Christian de Peretti
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Cointegration tests with conditional heteroskedasticity
Journal of Econometrics, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Tae-Hwy, Tse, Yiuman
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Reprint of: Generalized Autoregressive Conditional Heteroskedasticity
Journal of Econometrics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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COINTEGRATION RANK TESTING UNDER CONDITIONAL HETEROSKEDASTICITY [PDF]
Econometric Theory, 2009 We analyze the properties of the conventional Gaussian-based cointegrating rank tests of Johansen (1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models) in the case where the vector of series under test is driven by globally stationary, conditionally heteroskedastic (martingale difference) innovations. We first demonstrate that
CAVALIERE, GIUSEPPE +2 more
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Conditional Heteroskedasticity Forecasts Regime Shift in a Whole-Ecosystem Experiment
Ecosystems, 2012 Regime shifts in stochastic ecosystem models are often preceded by early warning signals such as increased variance and increased autocorrelation in time series.
David A Seekell +2 more
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Autoregressive Conditional Heteroskedasticity
2007All models discussed so far use the conditional expectation to describe the mean development of one or more time series. The optimal forecast, in the sense that the variance of the forecast errors will be minimised, is given by the conditional mean of the underlying model.
Gebhard Kirchgässner, Jürgen Wolters
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On the Efficiency of Conditional Heteroskedasticity Models
Review of Quantitative Finance and Accounting, 1998This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution, the GMM estimates obtained from the joint estimating ...
T. Y. Lee, Tony Wirjanto
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Mixture periodic autoregressive conditional heteroskedastic models
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohamed Bentarzi, Fayçal Hamdi 0002
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A Heteroskedasticity Test Robust to Conditional Mean Misspecification
Econometrica, 1992Summary: This paper proposes a new test statistic to detect the presence of heteroskedasticity. The proposed test does not require a parametric specification of the mean regression function in the first stage regression. The regression function is estimated nonparametrically by the kernel estimation method.
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