Functional quantization rate and mean regularity of processes with an application to Lévy processes

We investigate the connections between the mean pathwise regularity of stochastic processes and their L^r(P)-functional quantization rates as random variables taking values in some L^p([0,T],dt)-spaces (0 < p <= r). Our main tool is the Haar basis. We then emphasize that the derived functional quantization rate may be optimal (e.g., for Brownian motion or symmetric stable processes) so that the rate is optimal as a universal upper bound. As a first application, we establish the O((log N)^{-1/2}) upper bound for general Ito processes which include multidimensional diffusions. Then, we focus on the specific family of Levy processes for which we derive a general quantization rate based on the regular variation properties of its Levy measure at 0. The case of compound Poisson processes, which appear as degenerate in the former approach, is studied specifically: we observe some rates which are between the finite-dimensional and infinite-dimensional ``usual'' rates

[1]  Jacques Istas,et al.  Self‐similar Processes , 2010 .

[2]  Gilles Pagès,et al.  Functional quantization of a class of Brownian diffusions : A constructive approach , 2006 .

[3]  Gilles Pagès,et al.  Functional quantization for numerics with an application to option pricing , 2005, Monte Carlo Methods Appl..

[4]  Michael Scheutzow,et al.  High Resolution Quantization and Entropy Coding for Fractional Brownian Motion , 2005, math/0504480.

[5]  G. Pagès,et al.  Optimal quantizers for Radon random vectors in a Banach space , 2005, J. Approx. Theory.

[6]  W. Linde,et al.  Evaluating the small deviation probabilities for subordinated Lévy processes , 2004 .

[7]  Gilles Pagès,et al.  Sharp asymptotics of the Kolmogorov entropy for Gaussian measures , 2004 .

[8]  G. Pagès,et al.  Sharp asymptotics of the functional quantization problem for Gaussian processes , 2004, math/0410156.

[9]  T. Tarpey,et al.  Profiling Placebo Responders by Self-Consistent Partitioning of Functional Data , 2003 .

[10]  Harald Luschgy,et al.  Functional Quantization and Small Ball Probabilities for Gaussian Processes , 2003, Universität Trier, Mathematik/Informatik, Forschungsbericht.

[11]  M. Lifshits,et al.  Small deviations for fractional stable processes , 2003, math/0305092.

[12]  Thaddeus Tarpey,et al.  Clustering Functional Data , 2003, J. Classif..

[13]  Gilles Pagès,et al.  Functional quantization of Gaussian processes , 2002 .

[14]  S. Graf,et al.  Foundations of Quantization for Probability Distributions , 2000 .

[15]  S. Taylor,et al.  LÉVY PROCESSES (Cambridge Tracts in Mathematics 121) , 1998 .

[16]  D. Applebaum Stable non-Gaussian random processes , 1995, The Mathematical Gazette.

[17]  E. Eberlein,et al.  Hyperbolic distributions in finance , 1995 .

[18]  N. Herrndorf,et al.  Approximation of vector-valued random variables by constants , 1983 .

[19]  J. Geluk Π-regular variation , 1981 .

[20]  Steven M. Melimis Numerical methods for stochastic processes , 1978 .

[21]  Bert Fristedt,et al.  Sample Functions of Stochastic Processes with Stationary, Independent Increments. , 1972 .

[22]  P. Millar Path behavior of processes with stationary independent increments , 1971 .

[23]  S. Dereich The coding complexity of diffusion processes under L p [ 0 , 1 ]-norm distortion , 2006 .

[24]  Michael Scheutzow,et al.  On the Link Between Small Ball Probabilities and the Quantization Problem for Gaussian Measures on Banach Spaces , 2003 .

[25]  Pötzelberger Klaus,et al.  CLUSTERING AND QUANTIZATION BY MSP-PARTITIONS , 2001 .

[26]  R. Wolpert Lévy Processes , 2000 .

[27]  J. Bertoin Subordinators: Examples and Applications , 1999 .

[28]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[29]  A. Shiryaev,et al.  Limit Theorems for Stochastic Processes , 1987 .

[30]  J. Doob Stochastic processes , 1953 .