Geodesic
A stochastic programming approach to managing liquid asset portfolios
Kybernetika, Tome 46 (2010) no. 3, pp. 536-547 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

Maintaining liquid asset portfolios involves a high carry cost and is mandatory by law for most financial institutions. Taking this into account a financial institution's aim is to manage a liquid asset portfolio in an “optimal” way, such that it keeps the minimum required liquid assets to comply with regulations. In this paper we propose a multi-stage dynamic stochastic programming model for liquid asset portfolio management. The model allows for portfolio rebalancing decisions over a multi-period horizon, as well as for flexible risk management decisions, such as reinvesting coupons, at intermediate time steps. We show how our problem closely relates to insurance products with guarantees and utilize this in the formulation. We will discuss our formulation and implementation of a multi-stage stochastic programming model that minimizes the down-side risk of these portfolios. The model is back-tested on real market data over a period of two years
Maintaining liquid asset portfolios involves a high carry cost and is mandatory by law for most financial institutions. Taking this into account a financial institution's aim is to manage a liquid asset portfolio in an “optimal” way, such that it keeps the minimum required liquid assets to comply with regulations. In this paper we propose a multi-stage dynamic stochastic programming model for liquid asset portfolio management. The model allows for portfolio rebalancing decisions over a multi-period horizon, as well as for flexible risk management decisions, such as reinvesting coupons, at intermediate time steps. We show how our problem closely relates to insurance products with guarantees and utilize this in the formulation. We will discuss our formulation and implementation of a multi-stage stochastic programming model that minimizes the down-side risk of these portfolios. The model is back-tested on real market data over a period of two years
Classification : 90C15, 91G10, 91G80, 97M30
Keywords: stochastic programming; portfolio optimization; liquid assets 
@article{KYB_2010_46_3_a16,
     author = {Raubenheimer, Helgard and Kruger, Machiel F.},
     title = {A stochastic programming approach to managing liquid asset portfolios},
     journal = {Kybernetika},
     pages = {536--547},
     year = {2010},
     volume = {46},
     number = {3},
     mrnumber = {2676089},
     zbl = {1201.90142},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a16/}
}
TY  - JOUR
AU  - Raubenheimer, Helgard
AU  - Kruger, Machiel F.
TI  - A stochastic programming approach to managing liquid asset portfolios
JO  - Kybernetika
PY  - 2010
SP  - 536
EP  - 547
VL  - 46
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a16/
LA  - en
ID  - KYB_2010_46_3_a16
ER  - 
%0 Journal Article
%A Raubenheimer, Helgard
%A Kruger, Machiel F.
%T A stochastic programming approach to managing liquid asset portfolios
%J Kybernetika
%D 2010
%P 536-547
%V 46
%N 3
%U http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a16/
%G en
%F KYB_2010_46_3_a16
Raubenheimer, Helgard; Kruger, Machiel F. A stochastic programming approach to managing liquid asset portfolios. Kybernetika, Tome 46 (2010) no. 3, pp. 536-547. http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a16/

[1] BESA (Bond exchange of South Africa): An introduction to the BEASSA zero coupon yield curves. Bond exchange of South Africa. Johannesburg 2003.

[2] BESA (Bond exchange of South Africa): The BEASSA zero coupon yield curves: Technical specifications. Bond exchange of South Africa. Johannesburg 2003.

[3] Dempster, M. A. H., Germano, M., Madova, E. A., Rietbargen, M. I., Sandrini, F., Scrowston, M.: Managing guarantees. J. Portfolio Management 32 (2006), 51–61. | DOI

[4] Dempater, M. A. H., Pflug, G., Mitra, G., eds.: Quantitative Fund Management. Chapman & Hall/CRC Financial Mathematics Series, New York 2008.

[5] Hoyland, K., Wallace, S. W.: Generating scenario trees for multistage decision problems. Management Sci. 47 (2001), 2, 295–307. | DOI

[6] Kouwenberg, R.: Scenario generation and stochastic programming models for asset liability management. Europ. J. Oper. Res. 134 (2001), 279–292. | DOI | MR | Zbl

[7] Mulvey, J. M., Pauling, W. R., Madey, R. E.: Advantages of multiperiod portfolio models. J. Portfolio Management 29 (2001), 35–45. | DOI

[8] Shapiro, A., Dentcheva., D., Ruszczyński, A.: Lectures on Stochastic Programming: Modeling and Theory. MPS/SIAM Ser. Optim. 9, Philadelphia 2009. | MR

[9] South Africa: Banks Act, No. 94 of 1990 (As amended). 1990. Pretoria: Government Printer. [WEB:] http://www.reservebank.co.za

[10] South Africa: Regulations Relating to Banks. (Proclamation No. R. 1112, 2000). 2000. Government Gazette, 21726, Nov. 8. (Regulations Gazette No. 6917.) [WEB:] http://www.reservebank.co.za

[11] Zenios, S. A.: Practical Financial Optimization: Decision Making for Financial Engineers. Blackwell Publishing, 2008. | MR | Zbl

[12] Zenios, S. A., Ziemba, W. T.: Handbook of Asset and Liability Management: Volume 2. Elsevier, North-Holland, Amsterdam 2007.