Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model

Pipat Wongsaart and Jiti Gao

No 18/11, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A crucially important advantage of the semiparametric regression approach to the nonlinear autoregressive conditional duration (ACD) model developed in Wongsaart et al. (2011), i.e. the so-called Semiparametric ACD (SEMI-ACD) model, is the fact that its estimation method does not require a parametric assumption on the conditional distribution of the standardized duration process and, therefore, the shape of the baseline hazard function. The research in this paper complements that of Wongsaart et al. (2011) by introducing a nonparametric procedure to test the parametric density function of ACD error through the use of the SEMI-ACD based residual. The hypothetical structure of the test is useful, not only to the establishment of a better parametric ACD model, but also to the specification testing of a number of financial market microstructure hypotheses, especially those related to the information asymmetry in finance. The testing procedure introduced in this paper differs in many ways from those discussed in existing literatures, for example Aït-Sahalia (1996), Gao and King (2004) and Fernandes and Grammig (2005). We show theoretically and experimentally the statistical validity of our testing procedure, while demonstrating its usefulness and practicality using datasets from New York and Australia Stock Exchange.

Keywords: Duration model; hazard rates and random measures; nonparametric kernel testing. (search for similar items in EconPapers)
JEL-codes: C14 C41 F31 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2011-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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