A Quasi-locally Most powerful Test for Correlation in the conditional Variance of Positive Data

Brendan McCabe, Gael Martin and Keith Freeland

No 2/10, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A test is derived for short-memory correlation in the conditional variance of strictly positive, skewed data. The test is quasi-locally most powerful (QLMP) under the assumption of conditionally gamma data. Analytical asymptotic relative efficiency calculations show that an alternative test, based on the first-order autocorrelation coefficient of the squared data, has negligible relative power to detect correlation in the conditional variance. Finite sample simulation results con.rm the poor performance of the squares-based test for fixed alternatives, as well as demonstrating the poor performance of the test based on the first-order autocorrelation coefficient of the raw (levels) data. Robustness of the QLMP test, both to misspecification of the conditional distribution and misspecification of the dynamics is also demonstrated using simulation. The test is illustrated using financial trade durations data.

Keywords: Locally most powerful test; quasi-likelihood; asymptotic relative efficiency; durations data; gamma distribution; Weibull distribution. (search for similar items in EconPapers)
JEL-codes: C12 C16 C22 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2010-02-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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