Intraday dynamics of volatility and duration: Evidence from Chinese stocks

Pacific Basin Finance Journal

Volumn 20, Issue 3, 2012, Pages 329-348

  Liu, Chun ^a   Maheu, John M ^b  

^a Tsinghua University   (China)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

ACD; GARCH; High frequency data; Market microstructure; Transaction horizon

Indexed keywords

EID: 84855180482     PISSN: 0927538X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pacfin.2011.11.001     Document Type: Article

References (50)

1. Ait-Sahalia Y., Mykland P.A. The effects of random and discrete sampling when estimating continuous-time diffusions. Econometrica 2003, 71:483-549.
2. Andersen T.G., Bollerslev T. DM-dollar volatility: intraday activity patterns, macroeconomic announcements, and longer run dependencies. Journal of Finance 1998, 53:219-265.
3. Andersen T.G., Bollerslev T. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 1998, 39:885-905.
4. Andersen T.G., Bollerslev T., Diebold F.X. Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics 2007, 89(4):701-720.
5. Andersen T., Bollerslev T., Diebold F.X. Parametric and Nonparametric Volatility Measurement. Handbook of Financial Econometrics 2009, Vol 1. Elsevier. Ait-Sahalia, Hansen (Eds.).
6. Andersen T.G., Bollerslev T., Diebold F.X., Ebens H. The distribution of realized stock return volatility. Journal of Financial Economics 2001, 61:43-76.
7. Andersen T.G., Bollerslev T., Diebold F.X., Labys P. The distribution of exchange rate volatility. Journal of the American Statistical Association 2001, 96:42-55.
8. Andersen T.G., Bollerslev T., Huang X. A semiparametric framework for modelling and forecasting jumps and volatility in speculative prices. Working Paper 2006, Duke University.
9. Barndorff-Nielsen O.E., Shephard N. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society, Series B 2002, 64:253-280.
10. Barndorff-Nielsen O.E., Shephard N. Estimating quadratic variation using realised variance. Journal of Applied Econometrics 2002, 17:457-477.
11. Bauwens L., Giot P. The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks. Annales d'Economie et Statistique 2000, 60:117-149.
12. Bauwens L.P., Giot J., Grammig J., Veredas D. A comparison of financial duration models via density forecasts. International Journal of Forecasting 2004, 20:589-609.
13. Bauwens L., Veredas D. The stochastic conditional duration model: a latent factor model for the analysis of financial durations. Journal of Econometrics 2004, 119(2):381-412.
14. Bollerslev T., Kretschmer U., Pigorsch C., Tauchen G.E. A discrete-time model for daily s&p 500 returns and realized variations: jumps and leverage effects. Journal of Econometrics 2009, 50(2):151-166.
15. Cai B., Cai C., Keasey K. Which trades move prices in emerging markets?: evidence from China's stock market. Pacific-Basin Finance Journal 2006, 14(5):453-466.
16. Calvet L.E., Fisher A.J. Multifractal Volatility: Theory, Forecasting and Pricing 2008, Academic Press, New York.
17. Campbell J.Y., Lo A.W., MacKinlay A.C. The Econometrics of Financial Markets 1997, Princeton University Press.
18. Chong T., Su Q. On the comovement of A and H shares. Chinese Economy 2006, 39(5):68-86.
19. Corsi F. A simple long memory model of realized volatility. Journal of Financial Econometrics 2009, 7(2):174-196.
20. Corsi F., Kretschmer U., Mittnik S., Pigorsch C. The volatility of realized volatility. Econometric Reviews 2005, 27:46-78.
21. Dow J., Gorton G. Noise trading, delegated portfolio management, and economic welfare. Journal of Political Economy 1997, 105:1024-1050.
22. Dufour A., Engle R.F. Time and the price impact of a trade. Journal of Finance 2000, 55:2467-2498.
23. Easley D., O'Hara M. Time and the process of security price adjustment. The Journal of Finance 1992, 19:69-90.
24. Engle R.F. The econometrics of ultra high frequency data. Econometrica 2000, 68:1-22.
25. Engle R.F., Lee G.G.J. A long-run and short-run component model of stock return volatility. Cointegration, Causality, and Forecasting 1999, Oxford University Press. Engle, White, White (Eds.).
26. Engle R.F., Russell J.R. Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 1998, 66:1127-1162.
27. Engle R.F., Russell J.R. Analysis of high frequency data. Handbook of Financial Econometrics 2009, Y. Ait Sahalia, L.P. Hansen (Eds.).
28. Engle R.F., Sun Z. Forecasting volatility using tick by tick data. Working Paper 2005, Department of Finance, New York University.
29. Forsberg L., Ghysels E. Why do absolute returns predict volatility so well. Journal of Financial Econometrics 2007, 5(1):31-67.
30. Gelfand A.E., Dey D. Bayesian model choice: asymptotic and exact calculations. Journal Royal Statistical Society, B 1994, 56:501-514.
31. Geweke J. Evaluating the acurracy of sampling-based approaches to calculating posterior moments (with discussion). Bayesian Statistics 1992, 4:169-193. Oxford University Press, Oxford. J.M. Bernardo, J.O. Berger, A.P. Dawid, A.F.M. Smith (Eds.).
32. Geweke J. Bayesian comparison of econometric models. Working Paper 1995, Research Department, Federal Reserve Bank of Minneapolis.
33. Geweke J. Contemporary Bayesian Econometrics and Statistics 2005, John Wiley & Sons Ltd.
34. Geweke J., Whiteman C. Bayesian forecasting. Handbook of Economic Forecasting. Elsevier, Amsterdam (I) 2006, North-Holland. Elliott, Granger, Timmermann (Eds.).
35. Ghysels E., Gourieroux C., Jasiak J. Stochastic volatility duration models. Journal of Econometrics 2004, 119(2):413-433. Elsevier.
36. Ghysels E., Jasiak J. GARCH for irregularly spaced financial data: the ACD-GARCH model. Studies in Nonlinear Dynamics and Econometrics 1998, 2:133-149.
37. Grammig J., Maurer K.-O. Non-monotonic hazard functions and the autoregressive conditional duration model. Econometrics Journal 2000, 3(1):16-38.
38. Grammig J., Wellner M. Modeling the interdependence of volatility and inter-transaction duration processes. Journal of Econometrics 2002, 106:369-400.
39. Hamilton J.D., Jorda O. A model for the federal funds rate target. Journal of Political Economy 2002, 110:1135-1167.
40. Koop G. Bayesian Econometrics 2003, John Wiley & Sons Ltd.
41. Koop G., Potter S. Bayes factors and nonlinearity: evidence from economic time series. Journal of Econometrics 1999, 88:251-282.
42. Lee B., Rui O., Wu W. Market segmentation and stock prices discount in the chinese stock market: revisiting B-share discounts in the Chinese stock market. Asia-Pacific Journal of Financial Studies 2008, 37(1):1-40.
43. Lunde A. A generalized gamma autoregressive conditional duration model. Discussion Paper 1999, Aarlborg University.
44. Maheu J.M. Can GARCH models capture long-range dependence. Studies in Nonlinear Dynamics & Econometrics 2005, 9(4). (Article 1).
45. Maheu J.M., McCurdy T.H. Do high-frequency measures of volatility improve forecasts of return distributions?. Journal of Econometrics 2011, 160(1):69-76.
46. Muller U., Dacorogna M., Dave R., Olsen R., Pictet O., von Weizsacker J. Volatilities of different time resolutions - analyzing the dynamics of market components. Journal of Empirical Finance 1997, 4(2-3):213-239.
47. Oomen R. Properties of realized variance under alternative sampling schemes. Journal of Business and Economic Statistics 2006, 24(2):219-237.
48. Shao X., Lian Y., Yin L. Forecasting value-at-risk using high frequency data: the realized range model. Global Finance Journal 2009, 20(2):128-136.
49. Tian G., Guo M. Interday and intraday volatility: additional evidence from the Shanghai Stock Exchange. Review of Quantitative Finance and Accounting 2007, 28(3):287-306.
50. Zhang M., Russell J.R., Tsay R.S. A nonlinear autoregressive conditional duration model with applications to financial transaction data. Journal of Econometrics 2001, 104(1):179-207.