The private wealth management industry has now become a very significant industry due to continuing strong economic growth in specific regions of the world. This increase is currently driving a larger wealth management market creating greater opportunities for wealth advisors to leverage new technology with a view to acquiring new clients and boosting profits. As a result, competition among wealth advisory firms is increasing to find ways to improve existing client relationships and provide new tools to improve advisor efficiency. Current private banking tools are typically tax and estate planning geared towards one specific country and financial simulation software, relying on single period mean-variance optimization of the asset portfolio. These tools suffer from significant limitations and cannot satisfy the needs of a sophisticated clientele.

While some industry players have recently developed planning tools that model assets in a multi-period stochastic framework, asset-liability matching for individuals remains an area for exploration. This paper adapts Asset-Liability Management (ALM) techniques developed for institutional investors to the context of private banking customers. Asset-Liability Management (ALM) denotes the adaptation of the portfolio management process in order to handle the presence of various constraints relating to the commitments of an investor’s liabilities. We argue that portfolio optimization techniques used by institutional investors, e.g., pension funds, could usefully be transposed to the context of private wealth management because they have been engineered precisely to allow for the incorporation of an investor’s specific constraints, objectives and horizon in the portfolio construction process. Taking investors' liability constraints and specific objectives into account actually has a dramatic impact on asset allocation decisions. For example, clients who wish to maintain a given level of expenses for their retirement years will expect the investment process performed on their current wealth to be able to generate cash-flows sufficient to meet their consumption needs, which justifies a focus on inflation hedging that is not typically involved in a standard asset management solution.

As an illustration, we consider the situation of an investor who wishes to invest fixed annual contributions (€x) for a future expenditure, e.g., the purchase of a house in 5 years, for which the current value is normalized at €100. We introduce an explicit model for the dynamics of real estate prices and the exhibit below shows the impact of real estate price uncertainty on the value of the €100 payment scheduled to be paid in 5 years from now. As we can see, real estate price risk is significant, with a nominal amount to be secured equal to €156.59 on average and a €27.18 standard deviation.

In practical terms, the goal is to generate a lump sum payment at horizon date (5 years). It is not possible in general to find a perfect liability-matching portfolio. The existence of a perfect liability-matching portfolio is actually only ensured on the following two conditions: the investor must be able to borrow against future income and invest the present value of the future contributions at the initial date; and there must be an investment vehicle (e.g., REITS) with a payoff which is directly related to real estate price uncertainty. We test two different situations: an opportunity set containing stocks, bonds and TIPS and an opportunity set containing stocks, bonds, TIPS and real estate (modelled as an investment that will pay the compounded return on real estate). To generate comparable portfolios, we looked at the improvement in surplus volatility for a given level of expected surplus.

The graph shows the efficient frontier in both cases, while risk-return indicators are reported in the table. As expected, the presence of assets allowing investors to span real estate price uncertainty proves to be a key element in improving the efficient frontiers obtained from an ALM perspective. Looking for example at portfolio D and D’ in the table, we see that for the same level of expected surplus (12.60 in both cases), the surplus volatility at the optimal level reaches 21.95 when the opportunity set does not contain a real estate asset, while it merely amounts to 4.25, a dramatic risk reduction, when the real estate asset is included. Again this signals the relevance of an ALM approach to private wealth management: it is only by trying to fit the client liability constraints that truly optimal solutions can be proposed.

In the same vein, we also consider a number of other illustrations that are typical of standard private wealth management problems and show that optimal solutions are strongly affected by the presence of liability constraints. In particular, we focus on various pension-related objectives and consider an individual who is either already retired or still employed, and who seeks to ensure a stream of inflation-protected fixed payments, based either on a lump-sum contribution or a series of annual contributions. We also introduce a variety of bequest-related objectives.

In conclusion, we argue that it is not the performance of a particular fund nor that of a given asset class (including commodities or hedge funds) that will be the determining factor in the ability of private wealth management to meet investors’ expectations. What will prove to be the decisive factor is the private wealth manager’s ability to design an asset allocation solution that is a function of the kinds of particular risks to which the investor is exposed, as opposed to the market as a whole. Hence, an absolute return fund, often perceived as a natural choice in the context of private wealth management, would not be a satisfactory response to the needs of a client facing long-term inflation risk, where the concern is capital preservation in real, as opposed to nominal, terms. Similarly, a client whose objective would be related to the acquisition of a property would accept low and even negative returns in situations when real estate prices significantly decrease, but will not satisfy himself or herself with relatively high returns if such high returns are not sufficient to meet a dramatic increase 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 solution.

In other words, the success or failure of the satisfaction of the client’s long-term objectives is fundamentally dependent on an ALM exercise that aims to determine the proper strategic inter-classes allocation as a function of the client’s specific objectives and constraints. Asset management should only come next as a response to the implementation constraints of the ALM decisions. On the one hand, it is meant to deliver/enhance the risk and return parameters supporting the ALM analysis for each asset class. On the other hand, it can also allow for the management of short-term constraints, such as capital preservation at a given confidence level, which are not necessarily taken into account by an ALM optimization exercise, which by nature focuses on long-term objectives.

This study was sponsored by Pictet & Cie.