Read full paper at:
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51277#.VGGkBWfHRK0
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51277#.VGGkBWfHRK0
Author(s)
1Dipartimento di Matematica e Informatica, Università di Camerino,
Camerino, Italy.
2Dipartimento di Scienze Economiche, Università degli Studi di Verona, Verona, Italy.
3Dipartimento di Management, Università Politecnica delle Marche, Ancona, Italy.
4Dipartimento di Matematica “G. Castelnuovo”, Università di Roma “La Sapienza”, Roma, Italy.
2Dipartimento di Scienze Economiche, Università degli Studi di Verona, Verona, Italy.
3Dipartimento di Management, Università Politecnica delle Marche, Ancona, Italy.
4Dipartimento di Matematica “G. Castelnuovo”, Università di Roma “La Sapienza”, Roma, Italy.
We present a trading execution model that describes
the behaviour of a big trader and of a multitude of retail traders
operating on the shares of a risky asset. The retail traders are modeled
as a population of “conservative” investors that: 1) behave in a
similar way, 2) try to avoid abrupt changes in their trading strategies,
3) want to limit the risk due to the fact of having open positions on
the asset shares, 4) in the long run want to have a given position on
the asset shares. The big trader wants to maximize the revenue resulting
from the action of buying or selling a (large) block of asset shares in
a given time interval. The behaviour of the retail traders and of the
big trader is modeled using respectively a mean field game model and an
optimal control problem. These models are coupled by the asset share
price dynamic equation. The trading execution strategy adopted by the
retail traders is obtained solving the mean field game model. This
strategy is used to formulate the optimal control problem that
determines the behaviour of the big trader. The previous mathematical
models are solved using the dynamic programming principle. In some
special cases explicit solutions of the previous models are found. An
extensive numerical study of the trading execution model proposed is
presented. The interested reader is referred to the website:
http://www.econ.univpm.it/recchioni/finance/w19 to find material
including animations, an interactive application and an app that helps
the understanding of the paper. A general reference to the work of the
authors and of their coauthors in mathematical finance is the website:
http://www.econ.univpm.it/recchioni/finance.
KEYWORDS
Cite this paper
Fatone, L. , Mariani, F. , Recchioni, M. and
Zirilli, F. (2014) A Trading Execution Model Based on Mean Field
Games and Optimal Control. Applied Mathematics, 5, 3091-3116. doi: 10.4236/am.2014.519294.
[1] |
Bertsimas, D. and Lo, A.W.
(1998) Optimal Control of Liquidation Costs. Journal of Financial
Markets, 1, 1-50.
http://dx.doi.org/10.1016/S1386-4181(97)00012-8 |
[2] | Almgren, R. and Chriss, N. (2000) Optimal Execution of Portfolio Transactions. Journal of Risk, 3, 5-39. |
[3] |
Almgren, R. (2003) Optimal
Execution with Nonlinear Impact Functions and Trading Enhanced Risk.
Applied Mathematical Finance, 10, 1-18. http://dx.doi.org/10.1137/090763470 |
[4] |
Almgren, R. (2012) Optimal
Trading with Stochastic Liquidity and Volatility. SIAM Journal of
Financial Mathematics, 3, 163-181. http://dx.doi.org/10.1137/090763470 |
[5] |
Gatheral, J. and Schied, A.
(2011) Optimal Trade Execution under Geometric Brownian Motion in the
Almgren and Chriss Framework. International Journal of Theoretical and
Applied Finance, 14, 353-368.
http://dx.doi.org/10.1142/S0219024911006577 |
[6] |
Schied, A. (2013) Robust
Strategies for Optimal Order Execution in the Almgren-Chriss Framework.
Applied Mathematical Finance, 20, 264-286. http://dx.doi.org/10.1080/1350486X.2012.683963 |
[7] |
Ankirchner, S.,
Blanchet-Scalliet, C. and Eyraud-Loisel, A. (2012) Optimal Liquidation
with Directional Views and Additional Information. Working Paper: http://hal.archives-ouvertes.fr/hal-00735298 |
[8] |
Guéant, O. (2013) Execution and
Block Trade Pricing with Optimal Constant Rate of Participation.
http://arxiv.org/pdf/1210.7608v3.pdf |
[9] |
Guéant, O. and Lehalle, C.A.
(2013) General Intensity Shapes in Optimal Liquidation. Mathematical
Finance, Published Online. http://onlinelibrary.wiley.com/doi/10.1111/mafi.12052/pdf |
[10] |
Lasry, J.M. and Lions, P.L.
(2007) Mean Field Games. Japanese Journal of Mathematics, 2, 239-260.
http://dx.doi.org/10.1007/s11537-007-0657-8 |
[11] | Lachapelle, A. and Wolfram, M.T. (2011) On a Mean Field Game Approach Modeling Congestion and Aversion in Pedestrian Crowds. Transportation Research Part B: Methodological, 45, 1572-1589. |
[12] | Guéant, O., Lasry, J.M. and Lions, P.L. (2010) Mean Field Games and Oil Production. In: Lasry, J.M., Lautier, D. and Fessler, D., Eds., The Economics of Sustainable Development, Editions Economica, Paris, 139-162. |
[13] |
Lachapelle, A., Salomon, J. and
Turinici, G. (2010) Computation of Mean Field Equilibria in Economics.
Mathematical Models and Methods in Applied Sciences, 20, 567-588. http://dx.doi.org/10.1142/S0218202510004349 |
[14] |
Shen, M. and Turinici, G. (2012)
Liquidity Generated by Heterogeneous Beliefs and Costly Estimation.
Networks and Heterogeneous Media, 7, 349-361. http://dx.doi.org/10.3934/nhm.2012.7.349 |
[15] | Couillet, R., Perlaza, S.M., Tembine, H. and Debbah, M. (2012) Electric Vehicles in the Smart Grid: A Mean Field Game Analysis. IEEE Journal on Selected Areas in Communications: Smart Grid Communications Series, 30, 1086-1096. |
[16] | Guéant, O., Lasry, J.M. and Lions, P.L. (2011) Mean Field Games and Applications. In: Cousin, A., Crépey, S., Guéant, O., Hobson, D., Jeanblanc, M., Lasry, J.M., et al., Eds., Paris-Princeton Lectures on Mathematical Finance 2010, Lecture Notes in Mathematics, Springer, Berlin, 205-266. |
[17] |
Kalman, R.E. (1960) A New
Approach to Linear Filtering and Prediction Problems. Journal of Basic
Engineering, 82, 35-45. http://dx.doi.org/10.1115/1.3662552 |
[18] |
Guéant, O. (2009) A Reference
Case for Mean Field Games Models. Journal de Mathématiques Pures et
Appliqués, 92, 276-294. http://dx.doi.org/10.1016/j.matpur.2009.04.008 |
[19] |
Stoer, J. and Bulirsch, R.
(1980) Introduction to Numerical Analysis. Springer-Verlag, New York.
http://dx.doi.org/10.1007/978-1-4757-5592-3 eww141111lx |
评论
发表评论