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Functional Relationships Between Price and Volatility Jumps and Their Consequences for Discretely Observed Data

Published online by Cambridge University Press:  30 January 2018

Jean Jacod*
Affiliation:
Université Pierre et Marie Curie
Claudia Klüppelberg*
Affiliation:
Technische Universität München
Gernot Müller*
Affiliation:
Technische Universität München
*
Postal address: Institut de Mathématiques de Jussieu, Université Pierre et Marie Curie, 4 Place Jussieu, 75 005 Paris, France. Email address: jean.jacod@upmc.fr
∗∗ Postal address: Centre for Mathematical Sciences, Technische Universität München, 85748 Garching, Germany.
∗∗ Postal address: Centre for Mathematical Sciences, Technische Universität München, 85748 Garching, Germany.
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Abstract

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Many prominent continuous-time stochastic volatility models exhibit certain functional relationships between price jumps and volatility jumps. We show that stochastic volatility models like the Ornstein–Uhlenbeck and other continuous-time CARMA models as well as continuous-time GARCH and EGARCH models all exhibit such functional relations. We investigate the asymptotic behaviour of certain functionals of price and volatility processes for discrete observations of the price process on a grid, which are relevant for estimation and testing problems.

Type
Research Article
Copyright
© Applied Probability Trust 

References

Aït-Sahalia, Y. (2002). Telling from discrete data whether the underlying continuous-time model is a diffusion. J. Finance 57, 20752112.Google Scholar
Aït-Sahalia, Y. and Jacod, J. (2007). Volatility estimators for discretely sampled Lévy processes. Ann. Statist. 35, 355392.CrossRefGoogle Scholar
Aït-Sahalia, Y. and Jacod, J. (2009). Testing for Jumps in a discretely observed process. Ann. Statist. 37, 184222.Google Scholar
Anderson, T. G., Bollerslev, T. and Diebold, F. X. (2003). Some like it smooth, and some like it rough. Tech. Rep., Northwestern University.Google Scholar
Barndorff-Nielsen, O. E. and Shephard, N. (2001). Non–Gaussian Ornstein–Uhlenbeck–based models and some of their uses in financial economics (with discussion). J. R. Statist. Soc. B 63, 167241.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E. and Shephard, N. (2006). Econometrics of testing for Jumps in financial economics using bipower variation. J. Financial Econometrics 4, 130.CrossRefGoogle Scholar
Brockwell, P. J. (2001). Lévy-driven CARMA processes. Ann. Inst. Statist. Math. 53, 113124.Google Scholar
Brockwell, P. J., Chadraa, E. and Lindner, A. (2006). Continuous-time GARCH processes. Ann. Appl. Prob. 16, 790826.Google Scholar
Carr, P., Geman, H., Madan, D. and Yor, M. (2003). The fine structure of asset returns: an empirical investigation. J. Business 75, 305333.Google Scholar
Eberlein, E. and Keller, U. (1995). Hyperbolic distributions in finance. Bernoulli 1, 281299.Google Scholar
Fasen, V., Klüppelberg, C. and Lindner, A. (2006). Extremal behavior of stochastic volatility models. In Stochastic Finance, eds Shiryaev, A. N. et al., Springer, New York, pp. 107155.Google Scholar
Haug, S. and Czado, C. (2007). An exponential continuous-time GARCH process. J. Appl. Prob. 44, 960976.Google Scholar
Haug, S., Klüppelberg, C., Lindner, A. and Zapp, M. (2007). Method of moment estimation in the COGARCH(1,1) model. Econom. J. 10, 320341.CrossRefGoogle Scholar
Huang, X. and Tauchen, G. (2005). The relative contribution of Jumps to total price variance. J. Financial Econometrics 3, 456499.Google Scholar
Jacod, J. and Protter, P. (2011). Discretization of Processes. Springer, Berlin.Google Scholar
Jacod, J. and Shiryaev, A. N. (2003). Limit Theorems for Stochastic Processes, 2nd edn. Springer, Berlin.CrossRefGoogle Scholar
Jacod, J. and Todorov, V. (2010). Do price and volatility Jump together? Ann. Appl. Prob. 20, 14251469.Google Scholar
Jacod, J., Klüppelberg, C. and Müller, G. (2012). Testing for non-correlation between price and volatility Jumps. Submitted.Google Scholar
Klüppelberg, C., Lindner, A. and Maller, R. (2004). A continuous-time GARCH process driven by a Lévy process: stationarity and second-order behaviour. J. Appl. Prob. 41, 601622.Google Scholar
Klüppelberg, C., Lindner, A. and Maller, R. (2006). Continuous time volatility modelling: COGARCH versus Ornstein-Uhlenbeck models. In From Stochastic Calculus to Mathematical Finance, eds Kabanov, Y., Lipster, R. and Stoyanov, J., Springer, Berlin, pp. 393419.Google Scholar
Klüppelberg, C., Maller, R. and Szimayer, A. (2011) The COGARCH: a review, with news on option pricing and statistical inference. In Surveys in Stochastic Processes, eds Blath, J., Imkeller, P. and Roelly, S., Eur. Math. Soc., Zürich, pp. 2958.CrossRefGoogle Scholar
Lee, S. S. and Mykland, P. A. (2008). Jumps in financial markets: a new nonparametric test and Jump dynamics. Rev. Financial Studies 21, 25352563.Google Scholar
Lindner, A. (2009). Continuous time approximations to GARCH and stochastic volatility models. In Handbook of Financial Time Series, eds Andersen, T. G. et al., Springer, Berlin, pp. 481496.Google Scholar
Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. J. Financial Econom. 3, 125144.CrossRefGoogle Scholar
Todorov, V. and Tauchen, G. (2006). Simulation methods for Lévy-driven CARMA stochastic volatility models. J. Business Econom. Statist. 24, 450469.Google Scholar