Advanced SearchSearch Tips
Stock return volatility based on intraday high frequency data: double-threshold ACD-GARCH model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Stock return volatility based on intraday high frequency data: double-threshold ACD-GARCH model
Chung, Sunah; Hwang, S.Y.;
  PDF(new window)
This paper investigates volatilities of stock returns based on high frequency data from stock market. Incorporating the price duration as one of the factors in volatility, we employ the autoregressive conditional duration (ACD) model for the price duration in addition to the GARCH model to analyze stock volatilities. A combined ACD-GARCH model is analyzed in which a double-threshold is introduced to accommodate asymmetric features on stock volatilities.
ACD;high frequency GARCH;double-threshold ACD-GARCH;
 Cited by
Allen, D., Ng, K. H. and Peiris, S. (2012). Estimating and simulatingWeibull models of risk or price durations: an application to ACD models, North American Journal of Economics and Finance, 25, 214-224.

Allen, D., Ng, K. H. and Peiris, S. (2013). The efficient modelling of high frequency transaction data: a new application of estimating functions in financial economics, Economics Letters, 120, 117-122. crossref(new window)

Bauwens, L. and Giot, P. (2003). Asymmetric ACD models: Introducing price information in ACD models, Empirical Economics, 28, 709-731. crossref(new window)

Bauwens, L. and Hautsch, N. (2009). Modelling Financial High Frequency Data Using Point Processes, Handbook of Financial Time Series, Springer.

Engle, R. F. (2000). The econometrics of ultra-high-frequency data, Econometrica, 68, 1-22. crossref(new window)

Engle, R. F. and Russell, J. R. (1997). Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model, Journal of Empirical Finance, 4, 187-212. crossref(new window)

Engle, R. F. and Russell, J. R. (1998). Autoregressive conditional duration: a new model for irregularly spaced transaction data, Econometrica, 66, 1127-1162. crossref(new window)

Godambe, V. P. (1985). The foundation of finite sample estimation in stochastic processes, Biometrika, 72, 419-428. crossref(new window)

Jo, S. P. (2010). A study on determinants of volatility in intra-day stock return: an application of UHFGARCH-Leverage model, M.A thesis, Hanyang University.

Park, S. N. and Kim, Y. J. (2014). Bayesian forecasting with nonlinear autoregressive conditional duration models, Journal of Industrial Economics and Business, 27, 1-33.

Tsay, R. S. (2010). Analysis of Financial Time Series, Third Ed. Wiley, New York.