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Private Wealth Management - September 23, 2009

Asset-Liability Management in Private Wealth Management

While the private banking industry is in general relatively well equipped on the tax planning side, with tools that can allow private bankers to analyse the situation of high net worth individuals operating offshore or in multiple tax jurisdictions, the software packages used on the financial simulation side often suffer from significant limitations and cannot satisfy the needs of a sophisticated clientele. In fact, most financial software packages used by private bankers to generate asset allocation recommendations rely on single-period mean-variance asset portfolio optimisation, a tactic that, for at least two reasons, cannot lead to proper strategic allocation. For one, optimisation parameters (expected returns, volatilities, and correlations) are defined as constant across time, a practice which is contradicted by empirical observation and does not make it possible to take into account the length of the investment horizon. For another, and most importantly perhaps, liability constraints and risk factors affecting them, such as inflation risk on targeted spending, are neither modelled nor explicitly taken into account in the portfolio construction process.

Overall, dealing with a private client usually involves a detailed analysis of the client's objectives, constraints, and risk-aversion parameters, sometimes on the basis of rather sophisticated approaches. Yet it is striking that once this information has been collected, and sometimes formalised, very little is done to tailor a portfolio to the client’s specific needs. In general, several profiles, expressed in terms of volatility or drawdown, are provided; in some instances a distinction in how the capital will eventually be accessed (annuities or lump-sum payment) is made, but the client's specific objectives, constraints, and associated risk factors are simply not taken into account in the design of the optimal allocation.

The objective of this new paper from EDHEC-Risk is to shed light on the ways new forms of welfare-improving financial innovation inspired by the use of asset-liability management techniques, originally developed for institutional money management, can be used in private wealth management. Asset-liability management (ALM) refers to the adaptation of the portfolio management process to the presence of constraints relating to the commitments represented by the investor's liabilities. We argue that suitable extensions of portfolio optimisation techniques used by institutional investors, e.g., pension funds, could be transposed to private wealth management, precisely because these techniques have been engineered to incorporate in the portfolio construction process an investor's specific constraints, objectives, and horizon, all of which can be summarised in a single state variable, the value of the "liability" portfolio. As such, our paper can be seen as an attempt to merge two somewhat separate strands of the literature, that is, the literature on long-term financial decisions for private investors, which has focused mostly on an asset-only perspective, and the literature on asset-liability management decisions, which have been analysed mostly from an institutional perspective (pension funds, insurance companies, or endowments). We do so by casting the long-horizon life-cycle investment problem in an asset-liability management framework suitable for the private wealth management context, which allows us to show that pursuing an assetonly strategy usually involves a substantial opportunity cost.

Broadly, taking an ALM approach leads to defining risk and return relative to a liability portfolio, a critical improvement on assetonly asset allocation models that fail to account for the presence of investment and/or consumption goals and objectives, such as preparing for retirement or for a real estate acquisition. As a result, taking an ALM approach leads to a focus on the liability-hedging properties of various asset classes, a focus that would, by definition, be absent from an asset-only perspective. We also present a series of numerical illustrations suggesting that the model introduced in this paper could be applied in several situations typical of private wealth management.

Overall, it is not the performance of a particular fund or that of a given asset class that will be the determinant in the ability to meet a private investor's expectations. Satisfaction of the investor's long-term objectives is fundamentally dependent on an ALM exercise whose aim is to determine the proper strategic inter-class allocation as a function of the investor's specific objectives, constraints, and time-horizon. In other words, what will prove decisive is the ability to design an asset allocation programme that depends on the particular risks to which the investor is exposed. Similarly, the very concept of a risk-free asset depends on the investor's time-horizon and on his objectives. Hence, a five-year zero-coupon Treasury bond will not prove a perfectly safe investment for a private investor interested in a real estate acquisition in five years. The actual risk-free asset in this context (which we call below the liabilityhedging portfolio) would instead be an asset perfectly correlated with real estate prices. More generally, an investor whose objective is the acquisition of property is likely to accept low and even negative returns when real estate prices are falling significantly but will not be satisfied with relatively high returns if these returns do not match dramatic increases in real estate prices. In such circumstances, a long-term investment in stocks and bonds, with a performance weakly correlated with real estate prices, would not be the right investment. Likewise, in a pension context, absolute returns, often perceived as a natural choice in private wealth management, would not be a satisfactory response to the needs of a private investor facing long-term inflation risk, where the concern is capital preservation in real terms. In other words, the first benefit of the ALM approach is perhaps its impact on the menu of asset classes, with a focus on including an asset that exhibits the highest possible correlation with the liability portfolio.

While ours is obviously a fairly stylised model, and while important effects such as taxes or mortality risk are not explicitly taken into account at this stage, we believe it is a significant first normative step towards a better understanding of private wealth management decisions. Our main contribution is to show that a significant fraction of the complexity of optimal asset allocation decisions for private investors can be captured through the introduction of a single additional state variable, the liability value, which can account in a parsimonious way for investors' specific constraints and objectives.

Our analysis has great potential implications for the wealth management industry. Indeed, it is often said that proximity to investors is the main raison d'être of private wealth managers and a key source of competitive advantages. Building on this proximity, private bankers should be ideally placed to better account for their clients' specific liability constraints when engineering an investment solution for them. Most private bankers actually implicitly promote an ALM approach to wealth management. In particular, they claim to account for the investor's goals and constraints. The technical tools involved, however, are often inappropriate. While the private client is routinely asked all kinds of questions about his current situation, goals, preferences, constraints, etc., the resulting service and product offering mostly boil down to a rather basic classification in terms of risk profiles. In this paper, we provide a formal framework suggesting that asset-liability management can ensure that private wealth managers are able to offer their clients investment programmes and asset allocation advice that truly meet their needs.

This research was carried out as part of the Private ALM research chair partnered by ORTEC Finance.