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Limit theorems for multivariate Brownian semistationary processes and feasible results

Published online by Cambridge University Press:  03 September 2019

Riccardo Passeggeri*
Affiliation:
Imperial College London
Almut E. D. Veraart*
Affiliation:
Imperial College London
*
* Postal address: Department of Mathematics, Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK.
* Postal address: Department of Mathematics, Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK.

Abstract

In this paper we introduce the multivariate Brownian semistationary (BSS) process and study the joint asymptotic behaviour of its realised covariation using in-fill asymptotics. First, we present a central limit theorem for general multivariate Gaussian processes with stationary increments, which are not necessarily semimartingales. Then, we show weak laws of large numbers, central limit theorems, and feasible results for BSS processes. An explicit example based on the so-called gamma kernels is also provided.

Type
Original Article
Copyright
© Applied Probability Trust 2019 

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References

Aldous, D. J. and Eagleson, G. K. (1978). On mixing and stability of limit theorems. Ann. Prob. 6, 325331.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E. and Schmiegel, J. (2009). Brownian semistationary processes and volatility/intermittency. In Advanced Financial Modelling, eds Albrecher, H., Runggaldier, W., and Schachermayer, W., Walter de Gruyter, Berlin, pp. 125.Google Scholar
Barndorff-Nielsen, O. E. and Shephard, N. (2004). Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics. Econometrica 72, 885925.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., Benth, F. E. and Veraart, A. E. D. (2013). Modelling energy spot prices by volatility modulated Lévy-driven Volterra processes. Bernoulli 19, 803845.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., Corcuera, J. M. and Podolskij, M. (2009). Multipower variation for Brownian semistationary processes (full version). CREATES research paper 2009-21, Aarhus University.Google Scholar
Barndorff-Nielsen, O. E., Corcuera, J. M. and Podolskij, M. (2009). Power variation for Gaussian processes with stationary increments. Stoch. Process. Appl. 119, 18451865.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., Corcuera, J. M. and Podolskij, M. (2011). Multipower variation for Brownian semistationary processes. Bernoulli 17, 11591194.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., Pakkanen, M. S. and Schmiegel, J. (2014). Assessing relative volatility/intermittency/energy dissipation. Electron. J. Statist. 8, 19962021.CrossRefGoogle Scholar
Bateman, H. (1954). Tables of Integral Transforms. McGraw-Hill, New York.Google Scholar
Bennedsen, M. (2017). A rough multi-factor model of electricity spot prices. Energy Econom. 63, 301313.CrossRefGoogle Scholar
Bennedsen, M., Lunde, A. and Pakkanen, M. (2017). Decoupling the short- and long-term behavior of stochastic volatility. Preprint. Available at https://arxiv.org/abs/1610.00332v2.Google Scholar
Bennedsen, M., Lunde, A. and Pakkanen, M. (2017). Hybrid scheme for Brownian semistationary processes Finance Stoch. 21, 931965.CrossRefGoogle Scholar
Billingsley, P. (1999). Convergence of Probability Measures, 2nd edn. John Wiley, New York.CrossRefGoogle Scholar
Corcuera, J. M. (2012). New central limit theorems for functionals of Gaussian processes and their applications. Methodology Comput. Appl. Prob. 14.3, 477500.CrossRefGoogle Scholar
Corcuera, J. M., Hedevang, E., Podolskij, M. S. and Pakkanen, M. (2013). Asymptotic theory for Brownian semi-stationary processes with application to turbulence. Stoch. Process. Appl. 123, 25522574.CrossRefGoogle Scholar
Granelli, A. (2017). Limit theorems and stochastic models for dependence and contagion in financial markets. Doctoral Thesis, Imperial College London.Google Scholar
Granelli, A. and Veraart, A. E. D. (2019). A central limit theorem for the realised covariation of a bivariate Brownian semistationary process. To appear in Bernoulli.CrossRefGoogle Scholar
Jacod, J. (1997). On continuous conditional Gaussian martingales and stable convergence in law. In Séminaire de Probabilités XXXI (Lecture Notes Math. 1655), Springer, Berlin, pp. 232246.CrossRefGoogle Scholar
Jacod, J. (2008), Asymptotic properties of realized power variations and related functionals of semimartingales. Stoch. Process. Appl. 118, 517559.CrossRefGoogle Scholar
Nourdin, I. and Peccati, G. (2012). Normal Approximations with Malliavin Calculus. From Stein’s Method to Universality (Camb. Tracts Math. 92). Cambridge University Press.CrossRefGoogle Scholar
Podolskij, M. and Vetter, M. (2010). Understanding limit theorems for semimartingales: a short survey. Statist. Neerlandica 64, 329351.CrossRefGoogle Scholar
Reed, M. and Simon, B. (1975). Methods of Modern Mathematical Physics, Vol. II. Academic Press, New York.Google Scholar
Van der Vaart, A. W. (1998). Asymptotic Statistics. Cambridge University Press.CrossRefGoogle Scholar
Van der Vaart, A. W. and Wellner, J. A. (1996). Weak Convergence and Empirical Processes: with Applications to Statistics. Springer, New York.CrossRefGoogle Scholar