Abstract
We quantize a multidimensional SDE (in the Stratonovich sense) by solving the related system of ODE’s in which the d-dimensional Brownian motion has been replaced by the components of functional stationary quantizers. We make a connection with rough path theory to show that the solutions of the quantized solutions of the ODE converge toward the solution of the SDE. On our way to this result we provide convergence rates of optimal quantizations toward the Brownian motion for \(\frac{1} {q}\)-Hölder distance, q > 2, in L p(ℙ).
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References
Billingsley, P.: Convergence of probability measure. Wiley Series in Probability and Mathematical Statistics. Wiley, New York, 253 p. (1968)
Coutin, L., Victoir, N.: Enhanced Gaussian processes and applications. ESAIM P&S 13, 247–269 (2009)
Cuesta-Albertos, J.A., Matrán, C.: The strong law of large numbers for k-means and best possible nets of Banach valued random variables. Probab. Theory Relat. Field. 78, 523–534 (1988)
Dereich, S.: The coding complexity of diffusion processes under L p[0, 1]-norm distortion, pre-print. Stoch. Process. Appl. 118(6), 938–951 (2008)
Dereich S., Fehringer F., Matoussi A., Scheutzow M.: On the link between small ball probabilities and the quantization problem for Gaussian measures on Banach spaces. J. Theor. Probab. 16, 249–265 (2003)
Friz, P.: Continuity of the Itô-map for Hölder rough paths with applications to the support theorem in Hölder norm. Probability and partial differential equations in modern applied mathematics, IMA Vol. Math. Appl. vol. 140, pp. 117–135. Springer, New York (2005)
Friz, P., Victoir, N.: Differential equations driven by Gaussian signals. Ann. Inst. Henri Poincar Probab. Stat. 46(2), 369–413 (2010)
Friz, P., Victoir, N.: Differential equations driven by Gaussian signals II. Available at arxiv:0711.0668 (2007)
Friz, P., Victoir, N.: Multidimensional stochastic differential equations as rough paths: theory and applications, Cambridge Studies in Advanced Mathematics, 670 p. (2010)
Graf, S., Luschgy, H.: Foundations of quantization for probability distributions. Lecture Notes in Mathematics, vol. 1730. Springer, Berlin (2000)
Graf, S., Luschgy, H., Pagès, G.: Functional quantization and small ball probabilities for Gaussian processes. J. Theor. Probab. 16(4), 1047–1062 (2003)
Graf, S., Luschgy, H., Pagès, G.: Optimal quantizers for Radon random vectors in a Banach space. J. Approx. Theor. 144, 27–53 (2007)
Graf, S., Luschgy, H., Pagès, G.: Distortion mismatch in the quantization of probability measures. ESAIM P&S 12, 127–153 (2008)
Lejay, A.: An introduction to rough paths. Séminaire de Probabilités YXXVII. Lecture Notes in Mathematics, vol. 1832, pp. 1–59 (2003)
Lejay, A.: Yet another introduction to rough paths. Séminaire de Probabilités XLII. Lecture Notes in Mathematics, vol. 1979, pp. 1–101 (2009)
Lejay, A.: On rough differential equations. Electron. J. Probab. 14(12), 341–364 (2009)
Lejay, A.: Global solutions to rough differential equations with unbounded vector fields, pre-pub. INRIA 00451193 (2010)
Luschgy, H., Pagès, G.: Functional quantization of stochastic processes. J. Funct. Anal. 196, 486–531 (2002)
Luschgy, H., Pagès, G.: Sharp asymptotics of the functional quantization problem for Gaussian processes Ann. Probab. 32, 1574–1599 (2004)
Luschgy, H., Pagès, G.: Functional quantization of a class of Brownian diffusions: a constructive approach. Stoch. Process. Appl. 116, 310–336 (2006)
Luschgy, H., Pagès, G.: High resolution product quantization for Gaussian processes under sup-norm distortion. Bernoulli 13(3), 653–671 (2007)
Luschgy, H., Pagès, G.: Functional quantization and mean pathwise regularity with an application to Lévy processes. Ann. Appl. Probab. 18(2), 427–469 (2008)
Luschgy, H., Pagès, G., Wilbertz, B.: Asymptotically optimal quantization schemes for Gaussian processes. ESAIM P&S 12, 127–153 (2008)
Lyons, T.: Interpretation and solutions of ODE’s driven by rough signals. Proc. Symp. Pure 1583 (1995)
Lyons, T.: Differential Equations driven by rough signals. Rev. Mat. Iberoamericana 14(2), 215–310 (1998)
Lyons, T., Caruana, M.J., Lévy, T.: Differential equations driven by rough paths. Lecture Notes in Mathematics, vol. 1908, 116 p. (Notes from T. Lyons’s course at École d’été de Saint-Flour (2004).) (2007)
Pagès, G., Printems, J.: Functional quantization for numerics with an application to option pricing. Monte Carlo Methods Appl. 11(4), 407–446 (2005)
Pagès, G., Printems, J.: Website devoted to vector and functional optimal quantization: http://www.quantize.maths-fi.com (2005)
Revuz, D., Yor, M.: Continuous martingales and Brownian motion, 3rd edn. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], 293, 602 p. Springer, Berlin (1999)
…cknowledgements
We thank A. Lejay for helpful discussions and comments about several versions of this work and F. Delarue and S. Menozzi for initiating our first “meeting” with rough path theory.
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Pagès, G., Sellami, A. (2011). Convergence of Multi-Dimensional Quantized SDE’s. In: Donati-Martin, C., Lejay, A., Rouault, A. (eds) Séminaire de Probabilités XLIII. Lecture Notes in Mathematics(), vol 2006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15217-7_11
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DOI: https://doi.org/10.1007/978-3-642-15217-7_11
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