EMPLOYEES PROVIDENT FUND (EPF) MALAYSIA
Employees Provident Fund (EPF)
Malaysia: Generic Models for Asset and Liability Management
Under Uncertainty
A thesis submitted for the degree of Doctor of
Philosophy
by
Siti Aida Sheikh Hussin
Department of Mathematical Sciences
School of Information Systems,
Computing & Mathematics
Brunel University
August 2012
Abstract
We describe Employees Provident Funds (EPF) Malaysia. We explain about Defined
Contribution and Defined Benefit Pension Funds and examine their similarities and differences. We also briefly discuss and compare EPF schemes in four
Commonwealth countries. A family of Stochastic Programming Models is developed for the Employees Provident Fund Malaysia. This is a family of ex-ante decision models whose main aim is to manage, that is, balance assets and liabilities. The decision models comprise Expected Value Linear Programming, Two Stage
Stochastic Programming with recourse, Chance Constrained Programming and
Integrated Chance Constraints Programming. For the last three decision models we use scenario generators which capture the uncertainties of asset returns, salary contributions and lump sum liabilities payments. These scenario generation models for Assets and liabilities were developed and calibrated using historical data. The resulting decisions are evaluated with in-sample analysis using typical risk adjusted performance measures. Out- of- sample testing is also carried out with a larger set of generated scenarios. The benefits of two stage stochastic programming over deterministic approaches on asset allocation as well as the amount of borrowing needed for each pre-specified growth dividend are demonstrated. The contributions of this thesis are i) an insightful overview of EPF ii) construction of scenarios for assets returns and liabilities with different values of growth dividend, that combine the
Markov population model with the salary growth model and retirement payments iii) construction and
Malaysia: Generic Models for Asset and Liability Management
Under Uncertainty
A thesis submitted for the degree of Doctor of
Philosophy
by
Siti Aida Sheikh Hussin
Department of Mathematical Sciences
School of Information Systems,
Computing & Mathematics
Brunel University
August 2012
Abstract
We describe Employees Provident Funds (EPF) Malaysia. We explain about Defined
Contribution and Defined Benefit Pension Funds and examine their similarities and differences. We also briefly discuss and compare EPF schemes in four
Commonwealth countries. A family of Stochastic Programming Models is developed for the Employees Provident Fund Malaysia. This is a family of ex-ante decision models whose main aim is to manage, that is, balance assets and liabilities. The decision models comprise Expected Value Linear Programming, Two Stage
Stochastic Programming with recourse, Chance Constrained Programming and
Integrated Chance Constraints Programming. For the last three decision models we use scenario generators which capture the uncertainties of asset returns, salary contributions and lump sum liabilities payments. These scenario generation models for Assets and liabilities were developed and calibrated using historical data. The resulting decisions are evaluated with in-sample analysis using typical risk adjusted performance measures. Out- of- sample testing is also carried out with a larger set of generated scenarios. The benefits of two stage stochastic programming over deterministic approaches on asset allocation as well as the amount of borrowing needed for each pre-specified growth dividend are demonstrated. The contributions of this thesis are i) an insightful overview of EPF ii) construction of scenarios for assets returns and liabilities with different values of growth dividend, that combine the
Markov population model with the salary growth model and retirement payments iii) construction and
References: (2005) A Scenario Approach of ALM Wantanabe, K. and Ziemba, W. T. (1994) The Russel-Yasuda Kasai Model: An Asset Liability Model for a Japanese Insurance Company using