Author
Listed:
- Bingbing Ji
- Zhiping Chen
- Giorgio Consigli
- Zhe Yan
Abstract
We formulate a stochastic optimization problem from the perspective of an investment committee responsible for Tier 1 social security pension policies and whose decisions are bound to have relevant economic and social consequences. The adopted modelling approach combines canonical multistage stochastic programming (MSP) with dynamic stochastic control (DSC): the first applies to the short-medium term, the second to the long-term. Through the combined framework, we are able to span a long planning horizon without jeopardizing the accuracy of scenario tree based medium-term planning. We apply this methodology to the Chinese pension system, which relies on two large reference areas for rural and urban populations. In this article, we concentrate on the ever-growing urban public pension system, which is facing significant challenges due to a declining workforce and a rapidly ageing population. This welfare area, originally conceived as a pay-as-you-go (PAYG) system, has undergone several recent reforms to enhance its long-term sustainability and reduce the interventions of the central government required to improve its funding condition. Among those relevant in our setting, is the reduction of policy constraints that until 2015 severely limited the possibility to invest in assets other than traditional, locally traded, long-term fixed income securities. We propose an optimization model in which the decisions of the investment management aim at significantly reducing central government interventions as a last resort liquidity provider and progressively improving the system funding condition. A rich set of computational and economic evidence is presented to validate the methodology and clarify its potential benefits to pension system efficiency.
Suggested Citation
Bingbing Ji & Zhiping Chen & Giorgio Consigli & Zhe Yan, 2022.
"Optimal long-term Tier 1 employee pension management with an application to Chinese urban areas,"
Quantitative Finance, Taylor & Francis Journals, vol. 22(9), pages 1759-1784, September.
Handle:
RePEc:taf:quantf:v:22:y:2022:i:9:p:1759-1784
DOI: 10.1080/14697688.2022.2092329
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:22:y:2022:i:9:p:1759-1784. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.