Asset-Liability Management & Liability Driven Investment

This white paper sets out to explain an important financial planning model called the asset liability management (ALM); in particular it discusses why in practice, optimum planning models are used. The ability to build an integrated approach which combines liability models with that of asset allocation decisions have proved desirable and more efficient in that it can lead to better ALM decisions. The role of uncertainty, and quantification of risk in these planning models is considered. This white paper will be of interest to corporate treasurers, to fund managers in the pension & insurance industry, and to analysts who support ALM models in different financial institutions.

We describe the structure of Employees Provident Funds (EPFs); EPF is the main retirement scheme for private sector employees of the four countries: Singapore, Malaysia, India and Sri Lanka. In this paper we compare the EPF plans for these four countries and describe the similarities and the differences in terms of contributions and accounts, dividends withdrawals and annuity, minimum sum, health benefits, investment and also performance. We also discuss the challenges faced by EPFs and possible ways to overcome them. The biggest challenge is due to the phenomenon of aging population, which occurs even faster in Asia than in the Western countries. EPFs are defined contribution pension schemes; thus, the increase in life expectancy brings the risk that participants could outlive their savings. Lastly we discuss the EPF framework from an ALM perspective.

In contrast to decision models which use in-sample scenarios we use out-of-sample scenarios to conduct simulation and decision evaluation including backtesting. We propose two simulation methodologies and describe six decision evaluation techniques that can be applied to test the performance of liability-driven investment (LDI) models. A good practice of evaluating the performance of funds is to apply risk-adjusted performance measures; we have chosen two widely applied ratios: the Sortino ratio and the funding ratio. We perform statistical tests and establish the quality of the portfolios. We report our empirical ! endings by testing an asset and liability management model for pension funds where we propose one deterministic linear programing and three stochastic programming models.

Asset and Liability Management (ALM) models have been recently recast as Liability-Driven Investment (LDI) models for making integrated financial decisions in pension schemes investment: matching and outperforming a pension plans liabilities. LDI has become extremely popular as the decision tool of choice for pension funds. Market developments and recent accounting and regulatory changes require a pension fund to adopt a new view on their asset allocation decision. We present a generic ALM problem cast as an LDI, which we represent through a family of four decision models: a deterministic linear programming model, a two-stage stochastic programming (SP) model incorporating uncertainty, a chance-constrained SP model and an integrated chance-constrained SP model. In the deterministic model, we study the relationship between PV01 matching and the required funding. In the model, we have two sources of randomness: liabilities and interest rates. We generate interest rate scenarios using the Cox, Ingersoll and Ross model and investigate the relationship between funding requirements and minimize the absolute deviation of the present value matching of the assets and liabilities over time. In the chance-constrained programming model, we limit the number of future deficit events by introducing binary variables and a user-specified reliability level. The fourth model has integrated chance constraints, which not only limits the events of underfunding, but also the amount of underfunding relative to the liabilities. All models employ a buy-and-hold strategy in a fixed-income portfolio, whereas recourse actions are only taken in borrowing and lending.

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This report shows evaluation and simulation techniques. This work is not only verifying these techniques but also comparing different mathematical programming approaches applied to a pension fund. The five approaches are discussed in detail in Schwaiger et al. (2007).

This report uses risk-adjusted performance measures to identify the quality of al- ternative decision models. This report uses the decision models introduced by Schwaiger et al. (2007) and the simulation and decision evaluation outcomes from Schwaiger et al. (2008). Risk-adjusted performance measures are used by fund managers to rank and com- pare their portfolio performance with peers. In this report we examine the perfor- mance of alternative decision models for pension funds and use two ratios, namely the Sortino and the Solvency ratio to measure their performance over time.

Traditional Asset and Liability Management(ALM) models have been recently re- cast as Liability Driven Investment (LDI) models for making integrated financial decisions in pension schemes investment: matching and outperforming liabilities. LDI has become extremely popular as a decision tool of choice for pension funds. The last decade experienced a fall in the equity markets while bond yields reached low levels. New regulations were introduced whereby liabilities were hard to meet. In the case of a deficit the pension fund trustees and employers have to agree on extra contributions to fill the deficit within 10 years time. The UK Accounting standard FRS17 (since 2001, replacing SSAP24) requires the assets to be mea- sured by their market value and liabilities measured by a projected unit method and a discount rate reflecting the market yields then available on AA rated cor- porate bonds of appropriate currency and term (see Accounting Standards Board Financial Reporting Standard 17). Furthermore, deficit or surplus has to be fully included on the balance sheet. In the Netherlands and the Nordic countries LDI models have become established; the UK, Italy and few other European countries are close followers of this trend. Traditionally, assets and liabilities were considered separate. In asset management the aim was to maximize return for a given risk level. However, the matching of the liabilities was not taken into consideration. The main argument was that assets should be made to grow faster than liabilities. The modern integrated approach to LDI considers the cash flow streams for in- vested assets which can be fixed income portfolios enhanced by interest rate swaps and in some cases includes added swaptions. We present an asset and liability management (ALM) problem for LDI, which we model using three approaches: a deterministic model, a stochastic model incorpo- rating uncertainty and a chance-constrained stochastic model. In the deterministic model we look at the relationship between PV 01 matching and the required fund- ing. PV 01 is the change of the net present value of a bond due to 0:01% positive parallel shift in the yield curve. In the stochastic programming model we have two sources of randomness: liabilities and interest rates. We generate interest rate scenarios, look at the relationship between funding requirements and minimize the deviation of the PV (present value) matching of the assets and liabilities over time. In the chance-constrained programming model we limit the number of future de/cit events by introducing binary variables and a user specified reliability level. The last model has integrated chance constraints, which not only limits the events of underfunding, but also the amount of underfunding relative to the liabilities. Fur- thermore, a fixed mix model is introduced for testing purposes only.