Dans cet article, nous considérons le problème d'estimation de la covariation de deux processus de diffusion observés de façon asynchrone. Nous nous plaçons dans le cadre présenté dans [Bernoulli 11 (2005) 359-379, Ann. Inst. Statist. Math. 60 (2008) 367-406] et établissons un développement asymptotique au second ordre de la loi de l'estimateur de Hayashi-Yoshida. Ce développement est valable pour les drifts aléatoires non-anticipatifs et pour des pas d'échantillonnage irréguliers, éventuellement aléatoires, mais indépendant des processus observés. L'approche utilisée pour obtenir les principaux résultats peut être décomposée en trois étapes. La première consiste à établir un développement au second-ordre de la loi de l'estimateur dans le cadre gaussien. La deuxième est l'obtention d'une décomposition stochastique de l'estimateur lui-même et la dernière est l'évaluation de la covariance de Malliavin. A titre d'exemple, nous calculons les constantes du développement au second ordre dans le cas où l'échantillonnage est obtenu par deux processus de Poisson indépendants.
In this paper, we consider the problem of estimating the covariation of two diffusion processes when observations are subject to non-synchronicity. Building on recent papers [Bernoulli 11 (2005) 359-379, Ann. Inst. Statist. Math. 60 (2008) 367-406], we derive second-order asymptotic expansions for the distribution of the Hayashi-Yoshida estimator in a fairly general setup including random sampling schemes and non-anticipative random drifts. The key steps leading to our results are a second-order decomposition of the estimator's distribution in the gaussian set-up, a stochastic decomposition of the estimator itself and an accurate evaluation of the Malliavin covariance. To give a concrete example, we compute the constants involved in the resulting expansions for the particular case of sampling scheme generated by two independent Poisson processes.
Mots clés : edgeworth expansion, covariation estimation, diffusion process, asynchronous observations, Poisson sampling
@article{AIHPB_2011__47_3_748_0, author = {Dalalyan, Arnak and Yoshida, Nakahiro}, title = {Second-order asymptotic expansion for a non-synchronous covariation estimator}, journal = {Annales de l'I.H.P. Probabilit\'es et statistiques}, pages = {748--789}, publisher = {Gauthier-Villars}, volume = {47}, number = {3}, year = {2011}, doi = {10.1214/10-AIHP383}, mrnumber = {2841074}, language = {en}, url = {http://archive.numdam.org/articles/10.1214/10-AIHP383/} }
TY - JOUR AU - Dalalyan, Arnak AU - Yoshida, Nakahiro TI - Second-order asymptotic expansion for a non-synchronous covariation estimator JO - Annales de l'I.H.P. Probabilités et statistiques PY - 2011 SP - 748 EP - 789 VL - 47 IS - 3 PB - Gauthier-Villars UR - http://archive.numdam.org/articles/10.1214/10-AIHP383/ DO - 10.1214/10-AIHP383 LA - en ID - AIHPB_2011__47_3_748_0 ER -
%0 Journal Article %A Dalalyan, Arnak %A Yoshida, Nakahiro %T Second-order asymptotic expansion for a non-synchronous covariation estimator %J Annales de l'I.H.P. Probabilités et statistiques %D 2011 %P 748-789 %V 47 %N 3 %I Gauthier-Villars %U http://archive.numdam.org/articles/10.1214/10-AIHP383/ %R 10.1214/10-AIHP383 %G en %F AIHPB_2011__47_3_748_0
Dalalyan, Arnak; Yoshida, Nakahiro. Second-order asymptotic expansion for a non-synchronous covariation estimator. Annales de l'I.H.P. Probabilités et statistiques, Tome 47 (2011) no. 3, pp. 748-789. doi : 10.1214/10-AIHP383. http://archive.numdam.org/articles/10.1214/10-AIHP383/
[1] Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev. 39 (1998) 885-905.
and .[2] The distribution of realized stock return volatility. J. Fin. Econ. 61 (2001) 43-76.
, , and .[3] The distribution of realized exchange rate volatility. J. Amer. Statist. Assoc. 96 (2001) 42-55. | MR | Zbl
, , and .[4] On one term Edgeworth correction by Efron's bootstrap. Sankhyā A 46 (1984) 219-232. | MR | Zbl
and .[5] Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Manuscript. Available at http://www.nuffield.ox.ac.uk/economics/papers/index2007and2008.aspx.
, , and .[6] Econometric analysis of realized volatility and its use in estimating stochastic volatility models. J. R. Stat. Soc. Ser. B Stat. Methodol. 64 (2002) 253-280. | MR | Zbl
and .[7] Edgeworth expansions of suitably normalized sample mean statistics for atomic Markov chains. Probab. Theory Related Fields 130 (2004) 388-414. | MR | Zbl
and .[8] Edgeworth correction by bootstrap in autoregressions. Ann. Statist. 16 (1988) 1709-1722. | MR | Zbl
.[9] Long memory in continuous-time stochastic volatility models. Math. Finance 8 (1998) 291-323. | MR | Zbl
and .[10] Estimation of the coefficients of diffusion from discrete observations. Stochastics 19 (1986) 263-284. | MR | Zbl
and .[11] Comovements in stock prices in the very short run. J. Amer. Statist. Assoc. 74 (1979) 291-298.
.[12] On estimating the diffusion coefficient from discrete observations. J. Appl. Probab. 30 (1993) 790-804. | MR | Zbl
.[13] Edgeworth expansion for ergodic diffusions. Probab. Theory Related Fields 142 (2008) 1-20. | MR | Zbl
.[14] On the estimation of the diffusion coefficient for multi-dimensional diffusion processes. Ann. Inst. Henri Poincaré 29 (1993) 119-151. | Numdam | MR | Zbl
and .[15] Covariance measurement in the presence of non-synchronous trading and market microstructure noise. Preprint, 2006. Available at http://ssrn.com/abstract=912541. | MR
and .[16] The Bootstrap and Edgeworth Expansion. Springer, New York, 1992. | MR | Zbl
.[17] Consistent estimation of covariation under non-synchronicity. Stat. Inference Stoch. Process. 11 (2008) 93-106. | MR | Zbl
and .[18] On covariance estimation of non-synchronously observed diffusion processes. Bernoulli 11 (2005) 359-379. | MR | Zbl
and .[19] Nonsynchronous covariance estimator and limit theorem. Preprint, 2006.
and .[20] Asymptotic normality of a covariance estimator for non-synchronously observed diffusion processes. Ann. Inst. Statist. Math. 60 (2008) 367-406. | MR
and .[21] Nonsynchronous covariance estimator and limit theorem II. Preprint, 2008. | MR
and .[22] Nonparametric estimation methods of integrated multivariate volatilities. Working paper, 2006.
, , and .[23] On processes with conditional independent increments and stable convergence in law. Semin. Probab. Strasbourg 36 (2002) 383-401. | Numdam | MR | Zbl
.[24] Asymptotic expansion of M-estimators with long-memory errors. Ann. Statist. 25 (1997) 818-850. | MR | Zbl
and .[25] Estimation of diffusion processes from discrete observations. Scand. J. Statist. 24 (1997) 211-229. | MR | Zbl
.[26] An econometric analysis of non-synchronous trading. J. Econometrics 45 (1990) 181-211. | MR | Zbl
and .[27] Fourier series method for measurement of multivariate volatilities. Finance Stoch. 6 (2002) 49-61. | MR | Zbl
and .[28] Asymptotic expansions for martingales. Ann. Probab. 21 (1993) 800-818. | MR | Zbl
.[29] A Gaussian calculus for inference from high frequency data. Technical Report 563, Dept. Statistics, Univ. Chicago.
.[30] Anova for diffusions and Ito processes. Ann. Statist. 34 (2006) 1931-1963. | MR
and .[31] The Malliavin Calculus and Related Topics, 2nd edition. Springer, Berlin, 2006. | MR | Zbl
.[32] Consistent realized covariance for asynchronous observations contaminated by market microstructure noise. Manuscript. Available at http://www.palandri.eu/research.html.
.[33] Asymptotic theory for non-linear least square estimator for diffusion processes. Math. Oper. Statist. Ser. Stat. 14 (1983) 195-209. | MR | Zbl
.[34] Statistical inference from sampled data for stochastic processes. Contemp. Math. 80 (1988) 249-284. | MR | Zbl
.[35] Ultra high frequency volatility and co-volatility estimation in a microstructure model with uncertainty zones. Submitted.
and .[36] Asymptotic expansion under degeneracy. J. Japan Stat. Soc. 33 (2003) 145-156. | MR | Zbl
and .[37] Nonsynchronous data and the covariance-factor structure of returns. J. Finance 42 (1987) 221-231.
.[38] Probability, 2nd edition. Graduate Texts in Mathematics 95. Springer, New York, 1996. | MR | Zbl
.[39] Estimating betas from non-synchronous data. J. Fin. Econ. 5 (1977) 309-328.
and .[40] Speeds of convergence for the multidimensional central limit theorem. Ann. Probab. 5 (1977) 28-41. | MR | Zbl
.[41] Integrated covariance estimation using high-frequency data in the presence of noise. Working paper. Presented at CIREQ Conference on Realized Volatility, 2006.
and .[42] Estimation for diffusion processes from discrete observation. J. Multivariate Anal. 41 (1992) 220-242. | MR | Zbl
.[43] Malliavin calculus and asymptotic expansion for martingales. Probab. Theory Related Fields 109 (1997) 301-342. | MR | Zbl
.[44] Estimating covariation: Epps effect, microstructure noise. J. Econometrics (2010). To appear. | MR
.[45] Edgeworth expansions for realized volatility and related estimators. J. Econometrics (2010). To appear. | MR
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