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Autoregressive Conditional Duration Models

  • Ruey S. Tsay

Abstract

This chapter studies the autoregressive conditional duration model. It discusses properties and statistical inference of the model. It also considers some extensions to handle nonlinear durations and interventions. For applications, we apply the model to daily range of the log price of Apple stock and find that adopting the decimal system for the US stock price on January 29, 2001, significantly reduces price volatility.

Keywords

Standardize Residual Diurnal Pattern Duration Model Daily Range Federal Open Market Committee 
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Copyright information

© Ruey S. Tsay 2009

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  • Ruey S. Tsay

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