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 inJournal of theoretical probability Vol. 37; no. 1; pp. 549 - 581
Main Authors Khabou, Mahmoud, Privault, Nicolas, Réveillac, Anthony
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2024
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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.
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
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  organization: INSA de Toulouse, IMT UMR CNRS 5219, Université de Toulouse
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  givenname: Nicolas
  surname: Privault
  fullname: Privault, Nicolas
  organization: School of Physical and Mathematical Sciences, Nanyang Technological University
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  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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Issue 1
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
Language English
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Snippet We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals with respect to Hawkes processes by a normally...
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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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