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LIMIT THEOREMS FOR BIPOWER VARIATION IN FINANCIAL ECONOMETRICS

Published online by Cambridge University Press:  23 May 2006

Ole E. Barndorff-Nielsen
Affiliation:
University of Aarhus
Svend Erik Graversen
Affiliation:
University of Aarhus
Jean Jacod
Affiliation:
Université Pierre et Marie Curie
Neil Shephard
Affiliation:
University of Oxford

Abstract

In this paper we provide an asymptotic analysis of generalized bipower measures of the variation of price processes in financial economics. These measures encompass the usual quadratic variation, power variation, and bipower variations that have been highlighted in recent years in financial econometrics. The analysis is carried out under some rather general Brownian semimartingale assumptions, which allow for standard leverage effects.Ole E. Barndorff-Nielsen's work is supported by the Centre for Analytical Finance (CAF), which is funded by the Danish Social Science Research Council. Neil Shephard's research is supported by the UK's ESRC through the grant “High frequency financial econometrics based upon power variation.” We thank the editor, Peter Phillips, and the referees for their stimulating comments on an earlier version.

Type
Research Article
Copyright
© 2006 Cambridge University Press

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References

REFERENCES

Aït-Sahalia, Y. & J. Jacod (2005) Volatility Estimators for Discretely Sampled Lévy Processes. Manuscript, Department of Economics, Princeton University.
Andersen, T.G. & T. Bollerslev (1997) Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance 4, 115158.Google Scholar
Andersen, T.G. & T. Bollerslev (1998) Deutsche mark-dollar volatility: Intraday activity patterns, macroeconomic announcements, and longer run dependencies. Journal of Finance 53, 219265.Google Scholar
Andersen, T.G., T. Bollerslev, & F.X. Diebold (2003) Some Like It Smooth, and Some Like It Rough: Untangling Continuous and Jump Components in Measuring, Modeling and Forecasting Asset Return Volatility. Manuscript, Economics Department, Duke University.
Andersen, T.G., T. Bollerslev, & F.X. Diebold (2006) Parametric and nonparametric measurement of volatility. In Y. Aït-Sahalia & L.P. Hansen (eds.), Handbook of Financial Econometrics. North-Holland. Forthcoming.
Andersen, T.G., T. Bollerslev, F.X. Diebold, & P. Labys (2001) The distribution of exchange rate volatility. Journal of the American Statistical Association 96, 4255. Correction published in 2003, vol. 98, p. 501.Google Scholar
Andersen, T.G., T. Bollerslev, F.X. Diebold, & P. Labys (2003) Modeling and forecasting realized volatility. Econometrica 71, 579625.Google Scholar
Bandi, F.M. & J.R. Russell (2003) Microstructure Noise, Realized Volatility, and Optimal Sampling. Manuscript, Graduate School of Business, University of Chicago.
Barndorff-Nielsen, O.E., S.E. Graversen, J. Jacod, M. Podolskij, & N. Shephard (2006) A central limit theorem for realised power and bipower variations of continuous semimartingales. In Y. Kabanov & R. Lipster (eds.), From Stochastic Analysis to Mathematical Finance, Festschrift for Albert Shiryaev, pp. 3368. Springer.
Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde, & N. Shephard (2004) Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise. Unpublished paper, Nuffield College, Oxford.
Barndorff-Nielsen, O.E. & N. Shephard (2002) Econometric analysis of realised volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society, Series B 64, 253280.Google Scholar
Barndorff-Nielsen, O.E. & N. Shephard (2003) Realised power variation and stochastic volatility. Bernoulli 9, 243265. Correction published on pp. 1109–1111.Google Scholar
Barndorff-Nielsen, O.E. & N. Shephard (2004a) Econometric analysis of realised covariation: High frequency covariance, regression and correlation in financial economics. Econometrica 72, 885925.Google Scholar
Barndorff-Nielsen, O.E. & N. Shephard (2004b) Power and bipower variation with stochastic volatility and jumps (with discussion) Journal of Financial Econometrics 2, 148.Google Scholar
Barndorff-Nielsen, O.E. & N. Shephard (2005) How accurate is the asymptotic approximation to the distribution of realised volatility? In D.W.K. Andrews & J.H. Stock (eds.), Identification and Inference for Econometric Models. A Festschrift in Honour of T.J. Rothenberg, pp. 306331. Cambridge University Press.
Barndorff-Nielsen, O.E. & N. Shephard (2006a) Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics 4, 217252.Google Scholar
Barndorff-Nielsen, O.E. & N. Shephard (2006b) Variation, jumps, market frictions and high frequency data in financial econometrics. In Richard Blundell, Torsten Persson, & Whitney K. Newey (eds.), Advances in Economics and Econometrics. Theory and Applications, Ninth World Congress, Econometric Society Monographs. Cambridge University Press, forthcoming.
Barndorff-Nielsen, O.E., N. Shephard, & M. Winkel (2006) Limit theorems for multipower variation in the presence of jumps in financial econometrics. Stochastic Processes and Their Applications, forthcoming.Google Scholar
Calvet, L. & A. Fisher (2002) Multifractality in asset returns: Theory and evidence. Review of Economics and Statistics 84, 381406.Google Scholar
Christensen, K. & M. Podolskij (2005) Asymptotic Theory for Range-Based Estimation of Integrated Volatility of a Continuous Semi-Martingale. Manuscript. Aarhus School of Business.
Corradi, V. & W. Distaso (2006) Semiparametric comparison of stochastic volatility models via realized measures. Review of Economic Studies, forthcoming.Google Scholar
Delattre, S. & J. Jacod (1997) A central limit theorem for normalized functions of the increments of a diffusion process in the presence of round off errors. Bernoulli 3, 128.Google Scholar
Doob, J.L. (1953) Stochastic Processes. Wiley.
Forsberg, L. & E. Ghysels (2004) Why Do Absolute Returns Predict Volatility So Well? Unpublished paper, Economics Department, University of North Carolina, Chapel Hill.
French, K.R., G.W. Schwert, & R.F. Stambaugh (1987) Expected stock returns and volatility. Journal of Financial Economics 19, 329.Google Scholar
Ghysels, E., P. Santa-Clara, & R. Valkanov (2004) Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 5995.Google Scholar
Gloter, A. & J. Jacod (2001a) Diffusions with measurement errors, part I: Local asymptotic normality. ESAIM: Probability and Statistics 5, 225242.Google Scholar
Gloter, A. & J. Jacod (2001b) Diffusions with measurement errors, part II: Measurement errors. ESAIM: Probability and Statistics 5, 243260.Google Scholar
Goncalves, S. & N. Meddahi (2004) Bootstrapping Realized Volatility. Manuscript, CIRANO, Montreal.
Hall, P. & C.C. Heyde (1980) Martingale Limit Theory and Its Applications. Academic Press.
Hansen, P.R. & A. Lunde (2006) Realized variance and market microstructure noise (with discussion) Journal of Business & Economic Statistics 24, 127218.Google Scholar
Huang, X. & G. Tauchen (2005) The relative contribution of jumps to total price variation. Journal of Financial Econometrics 3, 456499.Google Scholar
Jacod, J. (1994) Limit of Random Measures Associated with the Increments of a Brownian Semimartingale. Preprint 120, Laboratoire de Probabilitiés, Université Pierre et Marie Curie, Paris.
Jacod, J. (1997) On continuous conditional Gaussian martingales and stable convergence in law. In Jacques Azema, M. Emery, & Marc Yor (eds.), Séminaire Probability XXXI, Lecture Notes in Mathematics, Volume 1655, pp. 232246. Springer-Verlag.
Jacod, J., A. Lejay, & D. Talay (2005) Testing the Multiplicity of a Diffusion. Manuscript, Laboratoire de Probabilitiés, Université Pierre et Marie Curie, Paris.
Jacod, J. & P. Protter (1998) Asymptotic error distributions for the Euler method for stochastic differential equations. Annals of Probability 26, 267307.Google Scholar
Jacod, J. & A.N. Shiryaev (2003) Limit Theorems for Stochastic Processes, 2nd ed. Springer-Verlag.
Karatzas, I. & S.E. Shreve (1991) Brownian Motion and Stochastic Calculus, 2nd ed. Graduate Texts in Mathematics 113. Springer-Verlag.
Karatzas, I. & S.E. Shreve (1998) Methods of Mathematical Finance. Springer-Verlag.
Maheswaran, S. & C.A. Sims (1993) Empirical implications of arbitrage-free asset markets. In P.C.B. Phillips (ed.), Models, Methods and Applications of Econometrics, pp. 301316. Basil Blackwell.
Mancini, C. (2004) Estimation of the characteristics of jump of a general Poisson-diffusion process. Scandinavian Actuarial Journal 1, 4252.Google Scholar
Martens, M. & D. van Dijk (2005) Measuring Volatility with the Realized Range. Manuscript, Econometric Institute, Erasmus University, Rotterdam.
Merton, R.C. (1980) On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics 8, 323361.Google Scholar
Munroe, M.E. (1953) Introduction to Measure and Integration. Addison-Wesley.
Mykland, P.A. & L. Zhang (2006) ANOVA for diffusions and Ito processes. Annals of Statistics 33, forthcoming.Google Scholar
Nielsen, M.O. & P.H. Frederiksen (2005) Finite Sample Accuracy of Integrated Volatility Estimators. Manuscript, Department of Economics, Cornell University.
Officer, R.R. (1973) The variability of the market factor of the New York stock exchange. Journal of Business 46, 434453.Google Scholar
Parkinson, M. (1980) The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 6166.Google Scholar
Phillips, P.C.B. & S. Ouliaris (1990) Asymptotic properties of residual based tests for cointegration. Econometrica 58, 165193.Google Scholar
Revuz, D. & M. Yor (1999) Continuous Martingales and Brownian Motion, 3rd ed. Springer-Verlag.
Rosenberg, B. (1972) The behaviour of random variables with nonstationary variance and the distribution of security prices. Working paper 11, Graduate School of Business Administration, University of California, Berkeley. Reprinted in N. Shephard, Stochastic Volatility: Selected Readings (Oxford University Press, 2005, pp. 83–108).
Schwert, G.W. (1989) Why does stock market volatility change over time? Journal of Finance 44, 11151153.Google Scholar
Schwert, G.W. (1990) Indexes of U.S. stock prices from 1802 to 1987. Journal of Business 63, 399426.Google Scholar
Schwert, G.W. (1998) Stock market volatility: Ten years after the crash. Brookings-Wharton Papers on Financial Services 1, 65114.Google Scholar
Shephard, N. (2005) Stochastic Volatility: Selected Readings. Oxford University Press.
Shiryaev, A.N. (1999) Essentials of Stochastic Finance: Facts, Models and Theory. World Scientific.
Woerner, J. (2006) Power and multipower variation: Inference for high frequency data. In A.N. Shiryaev, M. do Rosario Grossihno, P. Oliviera, & M. Esquivel (eds.), Proceedings of the International Conference on Stochastic Finance 2004, pp. 343364. Springer-Verlag.
Zhang, L. (2004) Efficient Estimation of Stochastic Volatility Using Noisy Observations: A Multi-Scale Approach. Manuscript, Department of Statistics, Carnegie Mellon University.
Zhang, L., P.A. Mykland, & Y. Aït-Sahalia (2005) A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association 100, 13941411.Google Scholar
Zhou, B. (1996) High-frequency data and volatility in foreign-exchange rates. Journal of Business & Economic Statistics 14, 4552.Google Scholar