Continuous time mean-variance portfolio optimization through the mean field approach
ESAIM: Probability and Statistics, Tome 20 (2016), pp. 30-44.

A simple mean-variance portfolio optimization problem in continuous time is solved using the mean field approach. In this approach, the original optimal control problem, which is time inconsistent, is viewed as the McKean–Vlasov limit of a family of controlled many-component weakly interacting systems. The prelimit problems are solved by dynamic programming, and the solution to the original problem is obtained by passage to the limit.

Reçu le :
Accepté le :
DOI : 10.1051/ps/2016001
Classification : 91G10, 49L20, 82C22, 60H10
Mots clés : Portfolio optimization, mean-variance criterion, optimal control, time inconsistency, dynamic programming, McKean–Vlasov limit, law of large numbers
Fischer, Markus 1 ; Livieri, Giulia 2

1 Department of Mathematics, University of Padua, via Trieste 63, 35121 Padova, Italy.
2 Scuola Normale Superiore di Pisa, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
@article{PS_2016__20__30_0,
     author = {Fischer, Markus and Livieri, Giulia},
     title = {Continuous time mean-variance portfolio optimization through the mean field approach},
     journal = {ESAIM: Probability and Statistics},
     pages = {30--44},
     publisher = {EDP-Sciences},
     volume = {20},
     year = {2016},
     doi = {10.1051/ps/2016001},
     mrnumber = {3528616},
     zbl = {1354.91143},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/ps/2016001/}
}
TY  - JOUR
AU  - Fischer, Markus
AU  - Livieri, Giulia
TI  - Continuous time mean-variance portfolio optimization through the mean field approach
JO  - ESAIM: Probability and Statistics
PY  - 2016
SP  - 30
EP  - 44
VL  - 20
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/ps/2016001/
DO  - 10.1051/ps/2016001
LA  - en
ID  - PS_2016__20__30_0
ER  - 
%0 Journal Article
%A Fischer, Markus
%A Livieri, Giulia
%T Continuous time mean-variance portfolio optimization through the mean field approach
%J ESAIM: Probability and Statistics
%D 2016
%P 30-44
%V 20
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/ps/2016001/
%R 10.1051/ps/2016001
%G en
%F PS_2016__20__30_0
Fischer, Markus; Livieri, Giulia. Continuous time mean-variance portfolio optimization through the mean field approach. ESAIM: Probability and Statistics, Tome 20 (2016), pp. 30-44. doi : 10.1051/ps/2016001. http://www.numdam.org/articles/10.1051/ps/2016001/

D. Andersson and B. Djehiche, A maximum principle for SDEs of mean-field type. Appl. Math. Optim. 63 (2011) 341–356. | DOI | MR | Zbl

S. Ankirchner and A. Dermoune, Multiperiod mean-variance portfolio optimization via market cloning. Appl. Math. Optim. 64 (2011) 135–154. | DOI | MR | Zbl

A. Bensoussan, K.C.J. Sung, S.C.P. Yam, and S.P. Yung, Linear-quadratic mean field games. Preprint arXiv:1404.5741 (2014). | MR

T. Björk and A. Murgoci, A general theory of Markovian time inconsistent stochastic control problems. Technical report, Stockholm School of Economics (2010).

T. Björk, A. Murgoci and X.Y. Zhou, Mean-variance portfolio optimization with state-dependent risk aversion. Math. Finance 24 (2014) 1–24. | DOI | MR | Zbl

R. Buckdahn, B. Djehiche and J. Li, A general stochastic maximum principle for SDEs of mean-field type. Appl. Math. Optim. 64 (2011) 197–216. | DOI | MR | Zbl

R. Carmona and F. Delarue, Forward-backward stochastic differential equations and controlled McKean–Vlasov dynamics. Preprint arXiv:1303.5835 (2013). | MR

R. Carmona, F. Delarue and A. Lachapelle, Control of McKean–Vlasov dynamics versus mean field games. Math. Fin. Econ. 7 (2013) 131–166. | DOI | MR | Zbl

W.H. Fleming and H.M. Soner, Controlled Markov Processes and Viscosity Solutions. Vol. 25 of Stoch. Model. Appl. Probab., 2nd edition. Springer, New York (2006). | MR | Zbl

D. Li and W.-L. Ng, Optimal dynamic portfolio selection: multiperiod mean-variance formulation. Math. Finance 10 (2000) 387–406. | DOI | MR | Zbl

H. Markowitz, Portfolio selection. J. Finance 7 (1952) 77–91.

H.P. Mckean, A class of Markov processes associated with nonlinear parabolic equations. Proc. Natl. Acad. Sci. USA 56 (1966) 1907–1911. | DOI | MR | Zbl

A.-S. Sznitman, Topics in propagation of chaos. In Ecole d’Eté de Probabilités de Saint-Flour XIX – 1989. Edited by P.-L. Hennequin. Vol. 1464 of Lect. Notes Math. Springer-Verlag, Berlin (1991) 165–251. | MR | Zbl

X.Y. Zhou and D. Li, Continuous-time mean-variance portfolio selection: a stochastic LQ framework. Appl. Math. Optim. 42 (2000) 19–33. | DOI | MR | Zbl

Cité par Sources :