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Garch Model Test Using High-Frequency Data

Chunliang Deng (), Xingfa Zhang (), Yuan Li () and Qiang Xiong ()
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Chunliang Deng: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Xingfa Zhang: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Yuan Li: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Qiang Xiong: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China

Mathematics, 2020, vol. 8, issue 11, 1-17

Abstract: This work is devoted to the study of the parameter test for the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Based on the daily GARCH model, using the parameter estimator obtained by intraday high-frequency data, the adjusted Likelihood Ratio test statistic and Wald test statistic are provided. Asymptotic distributions of the two adjusted test statistics are deducted and a way to select the optimal sampling frequency is also discussed. Simulation studies show that the proposed test statistics have better size and power than traditional ones (without using intraday high-frequency data). An empirical study is given to illustrate the potential applications of the proposed tests. The results show the idea of this article is of certain superiority and it can be extended to other GARCH type models.

Keywords: GARCH model; high-frequency data; likelihood ratio test; wald test (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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