ESTIMATING BIVARIATE GARCH-JUMP MODEL BASED ON HIGH FREQUENCY DATA: THE CASE OF REVALUATION OF THE CHINESE YUAN IN JULY 2005

Xinhong Lu (), Ken-Ichi Kawai () and Koichi Maekawa ()
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Xinhong Lu: Research Department, China Center for International Economic Exchanges, No. 22, Xi An Men Dajie, Xicheng District, Beijing 100017, China
Ken-Ichi Kawai: Department of Food and Nutrition, Beppu University, 82 Kita-ishigaki, Beppu, 874-8501, Japan
Koichi Maekawa: Faculty of Economics, Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima, 731-0192, Japan

Asia-Pacific Journal of Operational Research (APJOR), 2010, vol. 27, issue 02, 287-300

Abstract: This paper analyzes the behavior of one-minute high-frequency time-series data of exchange rates for five currencies (Japanese Yen, Australian Dollar, Canadian Dollar, Euro, and Pound Sterling) against the US Dollar when the Chinese Yuan was revalued on July 21st, 2005. The data show the following distinctive features: (1) There is a large jump in the exchange rates time series at the time of the Yuan revaluation. (2) Large volatility in the returns of exchange rates is observed for a while after the jump. (3) There are many other jumps, possibly correlated, in each exchange rate time series. To capture these features we fit the following models to the data: (i) a univariate GARCH-Jump model with a large jump that is influential on volatility, and (ii) a bivariate GARCH-Jump model with correlated Poisson jumps. For comparison, we also estimate these GARCH models without the associated jumps. The model performance is evaluated based on Value-at-Risk (VaR).

Keywords: High frequency data; bivariate GARCH-Jump model; correlated Poisson jumps; VaR threshold (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0217595910002697

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