Normal Approximation of Compound Hawkes Functionals
We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals with respect to Hawkes processes by a normally distributed random variable. In the case of deterministic and nonnegative integrands, our estimates involve only the third moment of the integrand in...
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Published in | Journal of theoretical probability Vol. 37; no. 1; pp. 549 - 581 |
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Language | English |
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Springer US
01.03.2024
Springer Nature B.V Springer |
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Abstract | We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals with respect to Hawkes processes by a normally distributed random variable. In the case of deterministic and nonnegative integrands, our estimates involve only the third moment of the integrand in addition to a variance term using a squared norm of the integrand. As a consequence, we are able to observe a “third moment phenomenon” in which the vanishing of the first cumulant can lead to faster convergence rates. Our results are also applied to compound Hawkes processes, and improve on the current literature where estimates may not converge to zero in large time or have been obtained only for specific kernels such as the exponential or Erlang kernels. |
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AbstractList | We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals with respect to Hawkes processes by a normally distributed random variable. In the case of deterministic and non-negative integrands, our estimates involve only the third moment of integrand in addition to a variance term using a square norm of the integrand. As a consequence, we are able to observe a "third moment phenomenon" in which the vanishing of the first cumulant can lead to faster convergence rates. Our results are also applied to compound Hawkes processes, and improve on the current literature where estimates may not converge to zero in large time, or have been obtained only for specific kernels such as the exponential or Erlang kernels. We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals with respect to Hawkes processes by a normally distributed random variable. In the case of deterministic and nonnegative integrands, our estimates involve only the third moment of the integrand in addition to a variance term using a squared norm of the integrand. As a consequence, we are able to observe a “third moment phenomenon” in which the vanishing of the first cumulant can lead to faster convergence rates. Our results are also applied to compound Hawkes processes, and improve on the current literature where estimates may not converge to zero in large time or have been obtained only for specific kernels such as the exponential or Erlang kernels. |
Author | Khabou, Mahmoud Privault, Nicolas Réveillac, Anthony |
Author_xml | – sequence: 1 givenname: Mahmoud surname: Khabou fullname: Khabou, Mahmoud organization: INSA de Toulouse, IMT UMR CNRS 5219, Université de Toulouse – sequence: 2 givenname: Nicolas surname: Privault fullname: Privault, Nicolas organization: School of Physical and Mathematical Sciences, Nanyang Technological University – sequence: 3 givenname: Anthony surname: Réveillac fullname: Réveillac, Anthony email: anthony.reveillac@insa-toulouse.fr organization: INSA de Toulouse, IMT UMR CNRS 5219, Université de Toulouse |
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Keywords | Normal approximation 60G57 Stein’s method 60G55 60H07 Malliavin calculus Hawkes processes 60F05 normal approximation Stein method Malliavin calculus Mathematics Subject Classification : 60G55 Hawkes processes Stein method normal approximation Malliavin calculus Mathematics Subject Classification : 60G55 |
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References | Peccati, Solé, Taqqu, Utzet (CR16) 2010; 38 Torrisi (CR18) 2016; 26 CR6 Hawkes (CR7) 1971; 1 CR8 Bacry, Delattre, Hoffmann, Muzy (CR1) 2013; 123 CR17 CR9 Privault (CR13) 2009 Privault (CR14) 2018; 15 Privault (CR15) 2019; 32 Nourdin, Peccati (CR10) 2009; 145 Chen, Goldstein, Shao (CR4) 2011 Picard (CR12) 1996; 32 Costa, Graham, Marsalle, Tran (CR3) 2020; 52 Daley, Vere-Jones (CR5) 1988 Ogata (CR11) 1981; 27 Brémaud, Massoulié (CR2) 1996; 24 M Costa (1233_CR3) 2020; 52 1233_CR17 GL Torrisi (1233_CR18) 2016; 26 E Bacry (1233_CR1) 2013; 123 N Privault (1233_CR13) 2009 P Brémaud (1233_CR2) 1996; 24 I Nourdin (1233_CR10) 2009; 145 N Privault (1233_CR14) 2018; 15 N Privault (1233_CR15) 2019; 32 Y Ogata (1233_CR11) 1981; 27 LHY Chen (1233_CR4) 2011 J Picard (1233_CR12) 1996; 32 DJ Daley (1233_CR5) 1988 AG Hawkes (1233_CR7) 1971; 1 1233_CR8 1233_CR6 G Peccati (1233_CR16) 2010; 38 1233_CR9 |
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SubjectTerms | Approximation Convergence Estimates Mathematical analysis Mathematics Mathematics and Statistics Probability Theory and Stochastic Processes Random variables Statistics |
Title | Normal Approximation of Compound Hawkes Functionals |
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