Evaluating financial time series models for irregularly spaced data: A spectral density approach

Duchesne, Pierre; Pacurar, Maria

doi:10.1016/j.cor.2006.02.017

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…From the result of this paper, this approach is questionable since the Box-Pierce statistic may not follow the usual χ 2 distribution asymptotically under the null hypothesis. A similar criticism has been pointed out in the univariate case, see Li and Yu (2003) and the discussion in Duchesne and Pacurar (2008). In fact, in a related context, it has been shown that the Box-Pierce-Ljung type test is invalid for GARCH models (Li and Mak, 1994) and multivariate GARCH models (Ling and Li, 1997).…”

Section: Introductionmentioning

confidence: 88%

“…Meitz and Teräsvirta (2006) devised a class of Lagrange multiplier (LM) tests against various forms of misspecification of the conditional expectations and showed that the LM test of no ACD effect on residuals is asymptotically equivalent to that proposed by Li and Yu (2003). Duchesne and Pacurar (2008) made use of a kernel spectral density estimator of the residuals to construct some adequacy tests for ACD models. Chen and Hsieh (2010) proposed a set of generalized moment tests for the conditional expectations based on the quasi-maximum exponential likelihood method.…”

Section: Introductionmentioning

confidence: 98%

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Diagnostic checking of the vector multiplicative error model

2016

Computational Statistics & Data Analysis

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Diagnostic checking of the vector multiplicative error model

2016

Computational Statistics & Data Analysis

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“…For instance, Li & Yu (2003) investigated the asymptotic distribution of the autocorrelations of the standardized durations, the residuals of the ACD model, which resulted in a chi-squared test that is more accurate than the ordinary portmanteau tests. On the other hand, Duchesne & Pacurar (2008) have dealt with the same problem using a spectral density approach. Hong & Li (2007) made use of the generalized spectrum concept to develop the generalized spectral derivative test.…”

Section: Introductionmentioning

confidence: 99%

On diagnostic checking of the autoregressive conditional intensity model

Kwok

2008

Can J Statistics

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The autoregressive conditional intensity model proposed by Russell (1998) is a promising option for fitting multivariate high frequency irregularly spaced data. The authors acknowledge the validity of this model by showing the independence of its generalized residuals, a crucial assumption of the model formulation not readily recognized by researchers. The authors derive the large-sample distribution of the autocorrelations of the generalized residual series and use it to construct a goodness-of-fit test for the model. Empirical results compare the performance of their test with other off-the-shelf tests such as the Ljung-Box test. They illustrate the use of their test with transaction records of the HSBC stock. Un test diagnostic pour le modèle autorégressifà intensité conditionnelleRésumé : Le modèle autorégressifà intensité conditionnelle proposé par Russell (1998) est une option prometteuse pour l'analyse de donnéesà haute fréquence età espacement irrégulier. Les auteurs attestent de la validité de ce modèle en démontrant l'indépendance de ses résidus généralisés, postulat crucial du modèle dont les chercheurs ne sont pas toujours conscients. Les auteurs déterminent la loi asymptotique des autocorrélations de la série des résidus généralisés et ils s'en servent pourélaborer un test d'adéquation du modèle. Des résultats empiriques comparent la performance de leur testà celle de procédures standards comme celle de Ljung-Box. Ils illustrent l'emploi de leur test au moyen de transactions sur le titre HSBC.

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“…Hautsch (2006) also considers some LM tests as well as various conditional moment tests and generalized conditional moment tests for conditional expected duration specification. Building on Hong (1996, 1997), Duchesne and Pacurar (2006) construct tests for the adequacy of ACD models, based on a kernel spectral density estimator of the standardized innovation process. They obtain a generalized version of the classical Box-Pierce-Ljung test statistic as a special case.…”

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confidence: 99%

Detecting misspecifications in autoregressive conditional duration models and non-negative time-series processes

Hong

Lee

2010

Journal of Time Series Analysis

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We develop a general theory to test correct specification of multiplicative error models of non-negative time-series processes, which include the popular autoregressive conditional duration (ACD) models. Both linear and nonlinear conditional expectation models are covered, and standardized innovations can have time-varying conditional dispersion and higher-order conditional moments of unknown form. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of parameter estimation uncertainty in finite samples, we adopt Wooldridge's (1990a) device to our context and justify its validity. Simulation studies show that in the context of testing ACD models, finite sample correction gives better sizes in finite samples and are robust to parameter estimation uncertainty. And, it is important to take into account timevarying conditional dispersion and higher-order conditional moments in standardized innovations; failure to do so can cause strong overrejection of a correctly specified ACD model. The proposed tests have reasonable power against a variety of popular linear and nonlinear ACD alternatives.

show abstract

Evaluating financial time series models for irregularly spaced data: A spectral density approach

Cited by 14 publications

References 32 publications

Diagnostic checking of the vector multiplicative error model

Diagnostic checking of the vector multiplicative error model

On diagnostic checking of the autoregressive conditional intensity model

Detecting misspecifications in autoregressive conditional duration models and non-negative time-series processes

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