Asymmetric Nonlinear Smooth Transition Garch Models

Anderson, Heather M.; Nam, Kiseok; Vahid, Farshid

doi:10.1007/978-1-4615-5129-4_10

Cited by 63 publications

(44 citation statements)

References 16 publications

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“…This restriction is imposed in order to avoid associating one of the regimes with the outlier observations that are present in our sample 1 . The ML estimates of the parameters of the five volatility models (assuming t-distributed innovations) are shown in Table 3, together with corresponding asymptotic standard errors.…”

Section: Empirical Applicationmentioning

confidence: 99%

“…We also report the value of the Ljung and Box (1978) portmanteau statistic (Q b ) based on the first b = 35 and b = 45 sample autocorrelations of the squared standardized residuals, the value of the maximized log-likelihood (L max ), and the value of the Akaike information criterion (AIC). 1 We also considered specifications in which the constant term in the volatility equation was regime-dependent. Such models were found to identify a few outliers as one regime (with no significant GARCH effects).…”

Section: Empirical Applicationmentioning

confidence: 99%

“…Our model differs from other members of the STGARCH family in two important respects. Firstly, unlike models where the argument of the mixing function used in the definition of the conditional variance is either the lagged (squared) innovation (e.g., Hagerud, 1996; González-Rivera, 1998; Lundbergh and Teräsvirta, 1998; Anderson et al, 1999;Lubrano, 2001; Medeiros and Veiga, 2009) or the lagged conditional variance (e.g., Lanne and Saikkonen, 2005), the mixing weights for C-STGARCH models are a function of both. Secondly, the mixing weights of a C-STGARCH model depend on all the parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

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Contemporaneous-Threshold Smooth Transition GARCH Models

Dueker¹,

Psaradakis

Solà

et al. 2011

Studies in Nonlinear Dynamics &Amp; Econometrics

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This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH) model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization to second conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. (2007) in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. A key feature of the C-STGARCH model is that its transition function depends on all the parameters of the model as well as on the data. The structural properties of the model are investigated, in addition to the finite-sample properties of the maximum likelihood estimator of its parameters. An application to U.S. stock returns illustrates the practical usefulness of the C-STGARCH model. * We would like to thank Nicolas Caramp for research assistance.

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Section: Empirical Applicationmentioning

confidence: 99%

Section: Empirical Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contemporaneous-Threshold Smooth Transition GARCH Models

Dueker¹,

Psaradakis

Solà

et al. 2011

Studies in Nonlinear Dynamics &Amp; Econometrics

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“…The GARCH literature o¤ers a variety of parameterizations for describing asymmetric e¤ects of shocks on the conditional variance. The ST-GARCH model, discussed in Hagerud (1997), González-Rivera (1998) and Anderson, Nam, and Vahid (1999), is one of them. Symmetry of the estimated TV-GARCH can be tested against asymmetry or, more generally, against nonlinearity, using these models as alternatives.…”

Section: Testing the Garch(p; Q) Component Against A Nonlinear Speci…mentioning

confidence: 99%

Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure

Amado¹,

Teräsvirta

2008

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in 1 AbstractIn this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either additive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change in the conditional and unconditional variances where the transition between regimes over time is smooth. A modelling strategy for these new time-varying parameter GARCH models is developed. It relies on a sequence of Lagrange multiplier tests, and the adequacy of the estimated models is investigated by Lagrange multiplier type misspeci…cation tests. Finite-sample properties of these procedures and tests are examined by simulation. An empirical application to daily stock returns and another one to daily exchange rate returns illustrate the functioning and properties of our modelling strategy in practice. The results show that the long memory type behaviour of the sample autocorrelation functions of the absolute returns can also be explained by deterministic changes in the unconditional variance.JEL classi…cation: C12; C22; C51; C52

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“…Smooth transition GARCH models are treated in Hagerud (1996), González-Rivera (1998) and Anderson, Nam, and Vahid (1999); see also Lundbergh and Teräsvirta (2002). In the present work, the smooth transition ACD(m,q) model is defined as follows:…”

Section: Smooth Transition Acd Modelsmentioning

confidence: 99%

Evaluating Models of Autoregressive Conditional Duration

Meitz¹,

Teräsvirta²

2006

Journal of Business & Economic Statistics

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This paper contains two novelties. First, a unified framework for testing and evaluating the adequacy of an estimated autoregressive conditional duration (ACD) model is presented. Second, two new classes of ACD models, the smooth transition ACD model and the timevarying ACD model, are introduced and their properties discussed.A number of new misspecification tests for the ACD class of models are introduced. They are Lagrange multiplier and Lagrange multiplier type tests against general forms of additive and multiplicative misspecification of the conditional mean function. These forms include tests against higher-order models, tests of no remaining ACD in the standardized durations, as well as tests of linearity and parameter constancy. In addition to its generality, the advantage of this testing approach is its ease of application, since all the resulting asymptotic null distributions are standard. The finite sample properties of the tests are investigated by simulation. A general observation is that the tests are well-sized and have good power. Versions of the test statistics robust to deviations from distributional assumptions other than those being explicitly tested are also given.The smooth transition and time-varying ACD models are introduced, their main properties are examined, and they serve as alternatives in the tests of linearity and parameter constancy. Finally, the tests are applied to ACD models of the IBM stock traded at the New York Stock Exchange.JEL classification: C22; C41; C52

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Asymmetric Nonlinear Smooth Transition Garch Models

Cited by 63 publications

References 16 publications

Contemporaneous-Threshold Smooth Transition GARCH Models

Contemporaneous-Threshold Smooth Transition GARCH Models

Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure

Evaluating Models of Autoregressive Conditional Duration

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