Asset Liability Management for Pension Fund - Article Example

Asset Liability Management for Pension Fund Asset Liability Management deals with the interaction of the sources and uses of funds that rum through banks financial statements. The growth of funded pensions and the increasing emphasis on risk management should strengthen the role of pension funds as stable, long-term institutional investors. However, this requires (among other priorities) that investment strategies more fully address the specific nature and structure of pension fund liabilities, thereby differentiating pension funds from many other institutional investors. Rather than seeking to report a profit or to outperform various indices, the ultimate purpose of DB pension schemes is to meet their future pension liabilities. In particular, this requires that the liabilities be covered by suitable assets (i.e., an ALM focus). However, pension fund investment and risk management practices have often focused more on asset returns than the actual liability structure of the pension balance sheet. In part, this is because assets are more easily adjusted in the short term to meet changing circumstances than pension liabilities, and because full actuarial recalculations typically only occur once every three years, with partial updates (e.g. reviewing assumptions such as inflation and prospective investment returns) only once a year or possibly every six months. One consequence of a limited focus on liabilities and ALM is that, in practice, many pension funds have pursued investment strategies measured relative to broad market indices. Recently, some pension funds and sponsors have also given thought to ways to manage liabilities more actively, including the conditionality of pension benefits (Frank J, 1997). Such flexibility would again impact pension fund investment and risk management/ALM practices. Recent regulatory and accounting changes, as well as market developments have put more focus on risk management and ALM practices. For example, the choice of the discount rate for minimum funding requirements increasingly influences pension fund asset allocation and investment strategies. Pension fund managers wishing to limit the volatility of their regulatory funding ratios may hold larger allocation of assets with a higher correlation (or matching) to the discount rate used for liabilities. Corporate bond yields are increasingly used by pension regulators as the relevant discount rate for liabilities (David 1995, Frank J 1997). Thus, investment by pension funds should be adequately regulated. These includes the need for an integrated assets/liability management approach for both institutional and function approaches, and the coordination of principles related to diversification, dispersion and matching by currency and maturity. Quantitative regulations, and prudent/person or expert principles should be carefully assessed, having regard to both the security and profitability objectives of pension funds. Self-investment should be limited, unless appropriate safeguard exist. Liberalization of investment abroad by pension funds should be promoted, subject to prudent management principles. Matching Principles of Asset Liability Management : Currency matching is a basic principle of investment management, but one that must be approached comprehensively. Derivatives might be used for this purpose if they help to achieve such a match. Also, matching the maturities of assets and liabilities is essential, and it is required that a framework of general principles be instituted. In this regard it is important that the regulation of the investment portfolio takes the portfolios of commitments into account. The maturity of pension funds play a key role in the investment strategies. The matching may, on the other side, be heavily influenced by various issues which affect the actual maturity of the products. The regulations of interests should integrate further the techniques related to assets/liabilities management (ALM). Mean - Variance Analysis : Mean-variance analysis was suggested by Markowitz. It is very widely used by practitioners. It is a systematic way for dealing with residual risk. However, the underlying assumptions are strict; either the returns have a multivariate normal distribution or the investors preference can be represented by some utility function over the mean and variance of the portfolio return (Stavros, 2006). Markowitz shows that for a wide range of utility functions and historical distributions, knowing the mean and variance of the distribution almost gives the expected utility of the distribution. It has been claimed that the downside risk framework offers a fundamentally better way to construct portfolios and determine asset allocations. To this end, comparisons of the two frameworks have been made in which mean- downside risk efficient portfolios unequivocally outperform mean variance efficient portfolios. In these studies, variances and lower partial moments are estimated using historical returns from the 1980s when equity markets performed extremely well, allowing mean down side risk efficient portfolios outperform mean variance efficient portfolios. There are two exceptional cases in which investors must pay close attention to downside risk. The first is that of modeling derivative risk, as the return of a derivative is often inherently asymmetric. The second is that of asset liability modeling, particularly when there is a small surplus (the difference between the assets and the liabilities) or a funding ratio (the ratio of the assets to the liabilities, expressed as a percentage) of about 100. An interest rate change, the present value of a pension plan's assets liabilities change in concert. When rates rise, the present value of future liabilities decreases, and when they drop, the present value of future liabilities increases. Liabilities behave like bonds, and with an appropriate asset allocation, the surplus can be made relatively insensitive to interest rates. If, however, the pension plan has an inadequate allocation to bonds, or to bonds of the wrong maturity, the surplus may be completely wiped out by a change in interest rates. In general, the pension fund has two sources for funding its liabilities: revenues from its asset portfolio (investment income and appreciation of the value of the portfolio) and contributions to the fund. Contributions are, by definition, made by the sponsor of the fund. Thus, at given point in time, the value of the assets of the fund is increased by receiving contributions and by appreciation of the value of invested assets, and it is decreased by making benefit payments. It is the responsibility of the pension fund to balance this process in such a way that the fund meets the solvency standards in force, and that all benefit payments, now and in the future, can be made timely. We propose a model that enables one to determine an ALM policy that minimizes the cost of funding while safeguarding the pension fund's ability to make all benefits payment timely, without becoming underfunded. This model is a mixed integer stochastic programming model which can be employed to determine dynamic ALM policies that are based on scenarios able to reflect any set of assumptions one chooses to make on future circumstances. The ALM model includes binary variables that enable one to count the number of times a certain event happens. This feature has been used to formulate chance constraints that are based on the probability distribution of states of the world that follows from the scenarios. For ALM, this property is used to model and restrict the probability of underfunding, both at the planning horizon, as well as at intermediate points in time. The model can be employed to determine a dynamic ALM strategy, consisting of an investment strategy and a contribution policy, which accounts for the development of liabilities. Decisions to be made at any point in time presume we will be able to make state-dependent recourse decisions in the future. They are the result of a trade-off between short and long term effects. Risk is reflected by the probability of under funding and the magnitude of deficits when they occur. The model accommodates the employment of realistic probability distributions of exogenous random variables. Asset-liability management attempts to find the optimal investment strategy under uncertainty in both the asset and liability streams. In the past, the two sides of the balance sheet have usually been separated, but simultaneous consideration of assets and liabilities can be very advantageous when they have common risk factors. By allocating assets such that they are highly correlated with the liabilities, one can increase returns and reduce risk. Developed in the late 1970's, immunization is an earlier ALM method that is still very popular today. Bond immunization attempts to match the interest rate sensitivity of a bond portfolio with the interest rate sensitivity of a liability stream. The resulting allocation only hedges against a small shift in the term structure of interest rates. This technique lacks the stochastic nature of interest rates and is a single stage model with no transaction costs. Therefore, immunization is inadequate for the multistage and stochastic problems of ALM. Stochastic programming is becoming more popular in finance as computing power increases. While multistage stochastic programs with recourse, for instance, can adequately model dynamic and stochastic financial problems, realistic ALM models could rarely be solved until recently and still some simplifications are usually needed to make the problems implementable. But now there have been enough advances that stochastic programming can obtain results superior to simple diversification or immunization. ALM and Stochastic Programming Models : Asset and Liability Management deals with uncertainty. They deal with the planning of financial resources in the face of uncertainty about economic, capital market, actuarial and demographic conditions. A general approach for dealing with uncertain data is to assign to the unknown parameters a probability distribution, which should then be incorporated into an appropriate mathematical programming model. Mathematical programming models for dealing with uncertainty are known as stochastic programs. Stochastic programming is recognized as a powerful modeling paradigm for several areas of application. Its validity for ALM problems, in particular, is enhanced by the fact that it readily incorporates in a common framework multiple correlated sources of risk for both the asset and liability side, has long time horizons, accommodates risk aversions, and allows for dynamic portfolio rebalancing while satisfying operational or regulatory restrictions and policy requirements. Pension fund management through Stochastic optimization : Most firms currently use static portfolio optimization, such as the Markowitz mean- variance allocation, which is short, sighted and when rolled forward can lead to radical portfolio rebalancing unless constrained by the portfolio manager. By contrast, the dynamic stochastic models incorporated in the system described below automatically hedge current portfolio allocations against future uncertainties over a longer horizon, leading to more robust decisions and previews of possible future problems and benefits. It is this feature and its ability to incorporate different attitudes to risk that make dynamic stochastic optimization the most natural framework for the effective solution of pension fund ALM problems. The Wilkie Stochastic investment model is just one of many approaches to investment modeling, all of which have different short comings. Unlike many alternative models, the Wilkie model has been fully published and therefore exposed to criticism. The Wilkie model differs in several fundamental ways from the models covered so far : ' It is a multivariate model, meaning that several related economic series are projected together. This is very useful for applications that require consistent projections of, for example, stock prices and inflation rates or fixed interest yields. ' The model is designed for long term applications. ' The model is designed to be applied to annual data. The Wilkie model makes assumptions about the stochastic process governing the evolution of a number of key economic variables. The integrated structure of the Wilkie model has made it particularly useful for actuarial applications. For the purpose of valuing equity-linked liabilities, this is useful if, for example, we assume liabilities depend on stock prices while reserves are invest in bonds. Also, for managed funds it is possible to project the correlated returns on bonds and stocks. Russel - Yasuda kasai model is another multistage stochastic programming model. It determines an optimal investment strategy that incorporates a multi period approach and enables the decision makers to define risks in tangible operational terms. The most important goal is to produce a high- income return to pay annual interest on saving-type insurance policies without sacrificing the goal of maximizing the long term wealth of the firm. (Constantin, 1997)An appropriate asset/liability management model had to include Yasuda's multiple and conflicting objectives combined with numerous regulatory restrictions. One type of regulations restricts the amount invested in certain asset classes both within the special savings accounts and on a firm wide basis. Another type of regulation restricts the sources and uses of funds. Thus, a useful asset/liability model would have to be able to balance the dual objectives of high income return to maintain and attract policy holders with the firm's desire to maximize its underlying capital. Conclusion: The market has witnessed an interesting evolution in the 1990s in methodologies for integrating liabilities into the asset allocation framework. One of he key issues of ALM is the determination of the amount of risky securities in the investments : if pension funds increase the amount of risky investments ( stocks and real estate), at the expense of fixed-income securities (bonds and loans), they can suffice with lower average contributions, at the expense of higher fluctuations of the contributions and a higher probability of the deficits. Traditional ALM, or linear forms of risk forecasting such as immunization, dedication, and optimization using linear programming, was state of the art for institutions at the beginning of the decade. At the same time, the foundation for the new modern ALM (MALM), which includes non linear forms of risk forecasting such as VAR and Monte Carlo simulation, was being laid in the trading side of financial institutions. Pension funds are just beginning to use a combination of VAR and simulation for their longer holding periods. Some insurance companies are making progress transitioning from immunization and optimization to more robust VAR and simulation models. All are taking direct account of volatilities and correlations among instruments. REFERENCES : ' Andreas, Stavros (2006): Handbook of Asset and Liability Management. Elsevier Publishers. ' Blake, David (1995): Pension Schemes and Pension funds in UK. Oxford University Press. ' Fabozzi, Frank J (1997): Pension Fund Investment Management. John Wiley and Sons Publishers. ' Zopounidis, Constantin (1997) : New Operational Approaches for Financial Modelling. Springer Publishers. Read More