asset and liability modelling and management

Independent, Impartial, Objective

T H E TA M R I S C O N S U LTA N C Y

T E C H N I C A L E X C E R P T S

A S S E T L I A B I L I T Y M O D E L L I N G & M A N A G E M E N T

In order to meet present and future financial liabilities we need to be able to structure portfolios to meet these needs as and when they arise and, we need to be able to assess the ability of assets to meet these needs over time.

Asset liability modelling and management determines the structure of the portfolio needed to meet these needs and, whether or not these needs can actually be met from current assets and future savings.

Managing a client’s personal financial needs without asset liability modelling and management is like driving without a destination, without a map, without any idea over the terrain, weather or the amount of fuel needed to get you wherever you are going.

WHAT IS ASSET LIABILITY MODELLING & MANAGEMENT?

Asset Liability modelling models the interaction and the allocation of assets to meet liabilities over time while asset liability management manages the strategy and structure of assets to meet liabilities over time.

The management of personal financial needs is different from the management of institutional or corporate assets and liabilities.

Pension fund deficits can be made up by increased funding, while individuals, especially in retirement, are unlikely to be able to make up deficits. Surpluses are also applied differently.

This different emphasis on short and long term needs has focused pension fund planning on long term deficits, whereas individuals need to focus on short term deficits given that long term financial security can be placed at risk by inappropriate short term structure and assumptions.

OBJECTIVES OF ASSET LIABILITY MODELLING & MANAGEMENT

The objectives of personal asset liability modeling and management are twofold.

To assess the ability of assets to meet needs/liabilities over time.

To determine portfolio structure needed to balance short and long term financial assets against short and long term financial needs.

ALMM components

Asset liability modelling and management depends on the following components.

Long term liability modelling.

The modelling of economic and asset risk and returns.

Decision rules and investment disciplines needed to determine optimum portfolio structure.

Decision rules and investment planning disciplines needed to determine long term asset/liability modelling.

The TAMRIS Consultancy8 Algo Court, Willowdale, Ontario, Canada, M2M 3P1

Telephone 416 730 8103, E mail [email protected]://moneymanagedproperly.com

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mailto:[email protected]


Of the above, the determination of optimum asset allocation and, the generation of return assumptions for modelling, are the most important components. Together they provide both an economic and an investment framework for asset liability modelling and management.

CONVENTIONAL MODELLING & MANAGEMENT

There are typically two types of asset/liability modelling and management, the simple compound interest/capital withdrawal models used by many financial planning professionals and, the complex institutional asset liability modelling used by pension fund consultants.

Both of these options have significant drawbacks for the effective management of an individual’s long term assets and liabilities.

SIMPLE ASSET LIABILITY MODELLING

These are mostly simple compound interest structures1 based on an average annual return assumption on the one side and the deduction of liabilities on the other2.

These models are not intended to determine portfolio structure or to reflect the risks of portfolio structure and strategy.

Their biggest weakness is their linearity; investments do not rise in a straight line. Constant withdrawals in real life will expose portfolio capital during periods of significant stock market and economic risk if portfolios are not structured to meet these withdrawals.

Importantly these techniques have no method of integrating asset management with the management of liabilities.

INSTITUTIONAL ASSET LIABILITY MODELLING

Conventional asset liability modelling structures have, in the main, relied on modern portfolio theory (mean variance optimisation) to determine portfolio structure and increasingly complex scenario generation programmes to determine return boundaries.

The inherent weakness in conventional asset liability modelling is that returns used to determine portfolio structure are also the returns used to effect modelling.

The direct relationship between required return and portfolio selection places dependence on the success of both modelling and portfolio structure on the precise generation of long term return.

Liabilities which have a much more stable profile are a more sensible primary determinant of portfolio structure than return, which is more volatile and uncertain3.

Because of the uncertainty and risks, the calculation of future return has gone down the road of precision and complexity. Because return assumptions need to be precise to determine structure and modelling, this has also restricted their use in managing risk.

This has led to the increasing application of value at risk methodologies in assessing risks to a portfolio’s ability to meet liabilities.

Unfortunately, VAR is a short term risk indicator more suitable to managing business risks (i.e. investment banking) than long term asset/liability management risks.

1 These are often tied to fairly sophisticated tax modules.2 Their tax and pension technical detail can often be at a much higher level than the investment detail.3 . In particular, when liabilities are more important, over the short term, liabilities are more certain and returns more uncertain.



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Institutional asset liability modelling is not dynamically integrated with the actual management of assets. Pension fund actuarial reviews are traditionally every three years, a period over which market valuations and risk return relationships can change significantly. .

THE TAMRIS APPROACH

TAMRIS’s approach to asset liability modelling and management has three key differences.

Asset allocation between cash, fixed interest and equity investments is determined principally by “short term liability profiles” and over time by the relationship between assets and liabilities.

Assumptions used to determine portfolio allocation are different from those used to model assets and liabilities over time.

Asset liability modelling and management are integrated with the construction, the planning and the management of the individual portfolio.

TAMRIS simplifies the ALM problem while retaining the robustness of ALM as a risk management and investment planning tool.

TAMRIS ASSET LIABILITY MODELLING & MANAGEMENT DISCIPLINES

TAMRIS asset liability modeling and management is comprised of the following four components.

Liability modelling; the modelling of future inflows and outflows to the portfolio to produce a net liability profile.

Short term asset liability modelling – this optimizes the allocation to low risk assets to meet income and capital needs in the event of significant stock market and economic risk.

Long term asset liability modelling – this assesses the ability of an investor’s portfolio to meet short and long term financial needs in the event of significant financial and economic risk.

Return modelling – this determines conservative risk/return profiles for use within modelling.

LIABILITY MODELLING

The objective of liability modeling is to produce a lifetime liability profile for each client. This profile is the net of all inflows and outflows expected to impact on savings and expenditure over the client’s lifetime4.

Present and future expenditure needs

Present and future sources of income.

Size and timing of future liabilities.

Size and timing of expected future capital

The resulting liability profile is used to structure the portfolio (short term asset liability modelling) and underpins long term asset/liability modeling5and investment planning. The graph below shows the net saving and dissaving over time for an individual client profile.

4 Surplus = saving, deficit = dissaving.5 Investment planning disciplines and decision rules are key to integrating liability profiles with portfolio construction. See TAMRIS Investment Planning for further information.



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While there should be little difference between TAMRIS liability profiling and that produced by most advanced liability modelling, the difference is in its application.

PORTFOLIO OPTIMISATION AND SHORT TERM ASSET LIABILITY MODELLING & MANAGEMENT

The objective of asset liability management for individuals is to optimise the balance of short term and long term financial assets between short term and long term financial needs6.

Key to this is the optimisation of low risk allocation and structure to meet and manage the allocation to short term liabilities.

This process replaces mean variance optimisation in traditional asset liability modelling and management as the primary determinant of the allocation to cash, fixed interest and equity investments.

The short term model does not select the equity portfolio, but it does determine the amount to be applied to the equity portfolio. The equity portfolio is selected by the relationship between the short term liability profile7 and the client’s portfolio value over a specific time frame.

Objectives

The objective of short term asset liability modeling and management is the provision of income and capital security and includes the ability to increase income and capital needs in line with inflation, irrespective of current market and economic conditions.

Stock market crashes, bear markets and recessions should have little or no effect on the ability of a portfolio to supply planned income and capital needs.

THE PROCESS

Short term liability profile

A short term asset liability model optimises portfolio allocation to low risk assets and equities in accordance with a client’s short term (over a period of significant short term stock market and economic risk) liability profile. For example, see below.

6 See TAMRIS Why TAMRIS for further information regarding optimisation.7 Short term liability profile for equity portfolio selection looks at an average yield requirement over periods as long as 8 years.



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Annual deficit surplus

-25000

-20000

-15000

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0

5000

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25000

2001 2004 2007 2010 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 2052

Annual deficit surplus



Short term liability profileInflows OutflowsIncome Pension Capital Total Drawings Capital Total Deficit (-)

inflows inflows surplus (+)Jan-04 - - - - - 79,681 79,681 79,681- Feb-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Mar-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Apr-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 May-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Jun-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Jul-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275

Aug-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Sep-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Oct-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Nov-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Dec-04 4,000 1,441 - 5,441 4,167 - 4,167 1,275 Jan-05 45,120 16,631 - 61,751 50,000 - 50,000 11,751 Jan-06 42,413 16,005 - 58,418 50,000 - 50,000 8,418 Jan-07 39,868 15,417 - 55,285 50,000 - 50,000 5,285 Jan-08 37,476 14,864 - 52,340 50,000 - 50,000 2,340 Jan-09 35,227 14,344 - 49,572 50,000 - 50,000 428- Jan-10 15,171 30,878 45,842 91,892 50,000 - 50,000 41,892 Jan-11 11,673 33,031 - 44,704 50,000 - 50,000 5,296- Jan-12 10,972 32,599 - 43,572 50,000 - 50,000 6,428- Jan-13 10,314 32,194 - 42,507 50,000 - 50,000 7,493-

The above short term liability profile summary shows short term liabilities from 1 to 10 years. The “current” year is broken into monthly components allowing cash management for near term liabilities.

Net income and capital liabilities can be separately fed to the short term asset/liability optimiser.

Low risk liability allocation strategy and security selection

The model constructs the low risk portfolio by passing the client’s net real liability requirements through the organisation’s central low risk investment strategy and security selection8 and optimising the allocation to each security.

Figure 1 Low risk asset class and liability allocation

Allocation 3 month

s

4 to 12 month

s

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years

3

years

4

years

5

years

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years

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years

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years

9

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years

Short term cash management 100% 100% 100% 100% 0% 0% 0% 0% 0% 0% 0%

Secure low risk 0% 0% 0% 0% 90% 70% 60% 60% 50% 50% 40%

International cash & fixed 0% 0% 0% 0% 10% 20% 20% 20% 25% 25% 20%

Corporates 0% 0% 0% 0% 0% 10% 15% 15% 15% 15% 15%

Specialist low risk 0% 0% 0% 0% 0% 0% 5% 5% 10% 10% 15%

In house funds/managed 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 10%

The above table is an example of low risk asset class allocation strategy for a given liability time frame. In the example, the closer the liability, the more conservative the investment strategy, the longer term the liability, the greater the diversification into “higher risk” lower risk investments.

Assumptions used to determine low risk strategy and security selection are different from those used to determine modelling and low risk optimisation.

The interaction of the short term liability model and a firm’s low risk investment strategy is similar to traditional dedicated low risk portfolio modelling.

8 TAMRIS believes in dedicated, horizon matched, low risk portfolios with stress testing risk assumptions to fulfil the immunisation function.



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Unlike traditional dedicated low risk modelling the low risk allocation is determined by a dynamic interaction with the equity portfolio. The equity allocation is a function of the low risk allocation and vice versa, a necessary prerequisite of optimisation.

Figure 2 Secure low risk asset class portfolio

Security Year 1 Year 2 Year 3

Year 4 Year 5 year 6 Year 7 Year 8 Year 9 Year 10

Conv 9.75% 06 0% 0% 0% 100% 100% 0% 0% 0% 0% 0%Treasury 7.25% 2007 0% 0% 0% 0% 0% 100% 0% 0% 0% 0%Treasury 9% 2008 0% 0% 0% 0% 0% 0% 100% 0% 0% 0%Treasury 7% 2009 0% 0% 0% 0% 0% 0% 0% 100% 0% 0%

The above table is an example of a secure low risk” portfolio and is the pure dedicated or cash matched component of the low risk portfolio. For example, a client with a liability in year 4 would have 90% of that liability met by the fixed interest security used to meet that component. Each security is selected with a view to maturing at a specific time frame.

Optimisation

Optimisation means different things to different people. Within the type of low risk short term security model discussed, optimization determines the recommended allocation to a security after adjusting for low risk interest as well as equity dividends, inflation risk, par values, liquidity, credit and currency risk.

Simple dedicated portfolios will often allocate the capital needed and be left with the decision over where to invest portfolio interest. There is no complex reinvestment decision with this type of optimization process since all dividends and interest are applied to current liabilities.

Importantly, the model is able to perform an optimization analysis on existing portfolios (not just the recommended allocation) and derive a liquidity as well as allocation analysis needed to manage the ongoing allocation of portfolios.

It also adapts to investment planning functionality within advanced systems.

This type of optimization model works on both recommended and existing allocations and securities and transforms the complex job of managing personalised low risk portfolios to a simple asset liability management framework.

OUTPUTS

Short term optimization models should output a recommended low risk allocation and recommended low risk portfolio, personalized to each client’s liability profile.

They should also outputs a dynamic structural analysis of ongoing portfolios regarding the liquidity and allocation of the portfolio, key to managing and initiating change.

Income and capital security

The following is a graphical illustration of short term asset/liability income and capital security modelling.



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50,000

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1 2 3 4 5 6 7 8 9 10

Years

Cap

ital

at

sta

rt o

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ach

ye

ar

Cash Bonds



The chart only illustrates the security provided by low risk securities in the event that equity investments could not or should not be sold. This allows investors to take a longer term view of their stock market investments and to enable them to make a decision as to how much security they need.

A well structured low risk portfolio allows investors to ride out periods of significant risk without having to sell under valued equities to meet income and capital needs, provides a stable platform for the provision of long term income security and a discipline for selling highly valued equities.

Low risk portfolios should not be designed to be run down. During periods of fair to high market valuations, equities would be realised to rebuild the low risk portfolio and during extreme market valuations sensible management should ensure that security offered by the low risk portfolio would be significantly higher.

OPTIMISATION AND PERSONALISATION

Short term optimisation models are different from conventional asset liability modelling. In the past the application of dedicated low risk portfolios has suffered from the lack of a “dedicated” low risk allocation rationale and is one the reasons for the dominance of “projective funding”9. Traditional dedicated models cannot cope with a dynamic and integrated equity allocation.

Also, many institutional techniques are really only practical for the management of specific mandates. Immunisation in particular requires constant rebalancing of the low risk portfolio to ensure duration matches liabilities. Since new liabilities are always entering the short term section of the portfolio and the duration of the existing portfolio is constantly changing, the management of individual portfolios on an institutional basis is unnecessarily complex.

Many clients have a wide range of existing low risk assets including cash balances. In order to provide personalised asset liability modelling and management these need to be included.

Optimisation also needs to model and manage existing portfolios as their dynamics shift (time, liabilities, markets) as simply and efficiently as possible. As soon as a recommended portfolio is implemented it becomes an existing portfolio.

The only way you can optimise short and long term financial needs is to have a short term optimisation model. This is the main reason why MVO constructs do not have a short term low risk rationale for the modelling and personal management of assets and liabilities over time.

The short term optimiser provides an important dynamic framework for the management of assets and liabilities, risk and return. It tells you how much cash you need, how much low risk, where and when to hold it, how much in equities and when you need to realise.

9 Essentially the mean variance optimised portfolio where required return is used to construct and allocate.The TAMRIS Consultancy

8 Algo Court, Willowdale, Ontario, Canada, M2M 3P1Telephone 416 730 8103, E mail [email protected]

http://moneymanagedproperly.com

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Equity risk return management

Low risk asset/liability management

The management of the realisation of

return

DEFICIENCIES OF MODERN PORTFOLIO THEORY IN LIABILITY SPACE

Liability risks are more important determinants of portfolio allocation than normal investment risk or monthly volatility10.

For example, which of the following are the more important risks?

a) Normal monthly volatility

b) Significant investment under performance

c) Stock market crashes, bear markets, recessions.

While institutions which derive their earnings from financial services may be exposed to short term price movements, investors with liabilities and long term financial needs should not be so exposed. Volatility is not the primary risk measure for individual investors.

MVO constructs are deficient in determining optimum portfolio allocation for asset liability modelling and management, for the following reasons.

Portfolio allocation is not determined by the size and timing of liabilities, but by the required rate of return.

Required rates of return, unless over a series of short discrete time periods, incorporate little information regarding the size, timing and variance of portfolio income and capital liabilities.

Higher rates of return over certain time periods can actually mean higher liabilities requiring a more conservative investment strategy and, not a more aggressive investment strategy.

The risk is that higher required rates of return may be used to select higher risk portfolios, unsuitable for the client risk or liability profile.

The risk management and the rationale for the structure depend on the risk of the return profile. If the return assumptions are incorrect and events turn out contrary, the portfolio structure may no longer be able to guarantee liabilities.

Periods of long term capital depletion cannot be managed effectively. Most individual portfolios will be looking at long term depletion of capital in real terms.

It is illogical, that an uncertain variable (return) rather than a certain variable is used to structure and select portfolios.

ECONOMIC AND ASSET RETURN MODELLING

10 - see TAMRIS Risk Profiling and TAMRIS Valuation Allocation & Management for further information regarding the weakness of MVO with regard to liabilities



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It is impossible to predict the exact size and timing of future returns on assets, the size and timing of economic growth and the rate of change of economic variables or the risks to the size and timing of both.

Yet, many portfolio construction and asset liability modelling frameworks depend upon the precise calculation of such.

The time frame over which most asset/liability modelling is conducted means that with the exception of index-linked securities, it is impossible to assume anything other than a constrained long term return relationship between earnings growth, economic growth/business cycles, inflation and price relatives upon which to fix the return relationship.

Within this context, the objectives, framework and limitations of return modelling need to be clearly defined.

Question 1

Should the assumptions used to determine modelling be the same as the assumptions used to determine portfolio allocation and strategy?

The implications are important. If the assumptions should be separate, then the required rate of return, a conventional modelling objective, has no role in selecting portfolio structure.

It is important to note that portfolios selected on the required return and the liability profile can result in two entirely different portfolios.

Within conventional modelling, the required rate of return is used to select the portfolio and hence the return assumptions underlying the portfolio drive the asset liability modelling.

Within asset and liability management frameworks, it is the liability profile and risk aversion and not the required rate of return that is used to determine portfolio allocation and, over time the ongoing relationship between assets and liabilities. Short and long term return assumptions are for modelling risk and the risks to return only.

Question 2

What is the objective of return generation?

Should it be to predict the actual return, or should it be to assess the risks to return?

The implications are important. If you attempt to determine the actual return, the risk is that actual return may be less than the return needed to meet liabilities. Predicting actual return does not just involve predicting the growth rates of assets but the strategic shape and change of the portfolio. In this context, predicting return can never be an objective.

Focussing on the risks to return should ensure that return assumptions will remain relevant during periods of significant financial risk. As such, the structure of the portfolio should be able to accommodate short term variance of return from the average modelled without having to adjust structure or financial objectives.

The objective of return modelling should be to minimise the effects of stock market and economic risk on the ability of current and future assets to meet lifetime financial needs.

Question 3

Should portfolio structure cope with the risks of return assumptions and if so what is the impact of return assumptions on portfolio structure and financial security?



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The implications are important. The security of individual investors depends on the ability to meet ongoing liabilities and hence, on short term not average long term return assumptions. Portfolios derived from long term returns ignore the short term structures needed to manage short term risks to return.

Long term average returns versus actual return within liability space

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20,000

40,000

60,000

80,000

100,000

120,000

2000 2001 2002 2003

Average return Actual return

The above chart shows the dangers of using average long term returns where portfolios are used to meet current liabilities. It uses the S&P starting level for each year from 2000 to 2003 and withdrawals of 8,000 a year, taken at the start of the year. It compares this against the modelled return for a straight 10% annual return with 8,000 a year withdrawals.

You can drown in a river with an average depth of 12 inches and can run aground in a river with an average depth of 12 feet.

Question 4

Should return assumptions be relative, consistent and stable over the lifetime of the portfolio?

Raising return assumptions or keeping them constant after a period of economic and stock market strength will give the illusion of enhanced future security, increasing current expenditure and placing future security at risk.

Reducing return assumptions or keeping them constant after a fall in markets will cause an unnecessary retrenchment in expenditure and possibly unnecessary change to portfolio structure.

Both instances are the result of inappropriate risk/return modelling. The solution? A dynamic relationship between modelling and portfolio management.

Return assumptions need to be relative not constant, they need to fall as short term price relatives rise and rise as they fall

Question 5

Should return assumptions reflect the past and past relationships, or the future and future relationships?

The implications are important. Periods of high returns are often followed by periods of low returns. Historic price movements may have built into valuations significant long term liability risks. These risks need to be built into models of future return.

TAMRIS believes that modelling of future return should not be based on past data and that the actual relationship between past data and price variables varies over time.



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EQUITY RETURN MODELLING

It may be impossible to predict actual long term asset returns but it is not impossible to gauge the risks to return and hence develop a return profile within those risk parameters for asset liability modelling.

There are a number of techniques used by investment practitioners to determine return. These range from the very simple to the very complex. Most, if not all the techniques suffer from two major drawbacks.

Drawback 1 – historical returns

Historical return should not be used to determine future return or portfolio allocation.

TAMRIS Valuation, Allocation and Management states that the current price relatives are important in determining value, risk and allocation while long term return relationships obscure the investment opportunities and valuation anomalies around which investment strategy is based on.

The same observations hold for the use of historical returns in determining future returns.

While the historical relationships between economic and market variables are critical in understanding and developing frameworks to determine risk and return (this is valuation analysis), present relationships and their relative positions are more important than the past.

The main criticisms of using historical data are as follows.

Past return relationships include economic and market valuation biases; the reverse yield gap that used to exist and disappeared in the 1950s; the high inflation and low P/Es of the early 1980s; the economic growth, structural imbalances, low inflation and expanded P/Es of the 1990s. In order to repeat the return of the past we would effectively need to start from the same base.

Many mutual fund prospectuses used to show the performance of the stock market from the early 1980s to the end of the 1990s. This clearly was a misrepresentation of future return and current risks.

Long term returns marginalise short term risks. We all live in the short term and it is the risks to short term return that have the greatest effect on individual investors’ financial security. Portfolios structured for smooth long term return relationships but with short term liabilities cannot survive in the real world.

Multivariate regression analysis, relies on past relationships between equity prices/return and macro variables. The magnitude of the relationship is not a constant and is not a reliable one to base forecasts on. In fact, over the short term, price and price relatives have an inverse relationship with return. As prices rise, return per unit of current capital falls. Market movements are not randomly independent.

Perform a simple regression analysis on the US stock market prior to 2000 and you would have forecasted a continuous rise in the index at a time when sensible valuation analysis would have shown severe risks to future return.

Only if you adjust equity investment for a rise in price relatives and an adjustment for cyclical earnings can you come up with a reliable variable for determining long term future return. You either need to adjust the return assumption or adjust the capital value per unit of return. The absence of any such adjustment invalidates the use of past price relationships to determine future return.

It is the inverse relationship over short time periods between past risk and return and future risk and return that causes forecasts based on historical data to be unreliable for the management of



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personal financial needs. Only an analysis of current price and economic relatives can assess risks to return and the return profile.

Drawback 2 – Modern portfolio theory framework

Many of the drawbacks to current techniques are associated with modern portfolio theory and its structural constraints.

Having to operate in risk return space places the emphasis on volatility as opposed to liability risk. As a result you need to model the interaction of nominal and real asset returns over time. This is extremely complex.

Get your inflation forecast wrong and your low risk allocation is incorrect. On the other hand, real asset returns over the long term can discount inflation. Take away the need to jointly model nominal and real assets over time and your modelling problem is simplified.

Asset and liability management frameworks separate the low risk and the equity portfolios, the allocation to which is dependent on liabilities and risk aversion, simplifying the modelling problem11.

Importantly, focussing on liabilities as opposed to risk/returns for structure adds stability to the portfolio ensuring that its structure remains appropriate irrespective of conditions, a factor absent with MVO constructs.

MVO’s focus on static long term portfolio constructs as opposed to current valuation risks in determining portfolio structure has placed focus on long term average returns to the exclusion of short term risk and returns. We all live in the short term and all risks are in the current price and current price relationships.

CONVENTIONAL RISK/RETURN MODELLING

The types of return assumptions used in conventional asset liability modelling are noted below.

Historical return

Over short periods of time returns are uncertain, while over longer time periods market returns have fairly stable relationship with real economic growth.

Indeed much modelling uses historic returns to determine future returns for asset liability modelling, either as a straightforward average or as part of a database on which to perform stochastic modelling.

Unfortunately, over short time frames, which can be fairly lengthy, the returns on equities can be much lower than the historical average. Investors in the US market in 1966 would have had to wait until 1992 for the real return on capital invested to beat inflation (excluding dividends) and a similar time frame for investors in the UK stock market in 1970. In fact, if we analyse long term returns, we can identify numerous periods of poor returns following market and economic peaks.

The use of average long term returns do not adequately cover the risks to future returns since they can include significant valuation biases. This cuts both ways. Periods of low returns and high risk will also under value future returns.

Indeed some retail modelling tools were using average annual returns over periods of 10 and 15 years during the late 1990s. Investors using average long term returns at the end of the 1990s on their portfolios to determine future financial security would have experienced significant revisions of future financial security.

11 Over the short term, allocation to low risk and equities can be enhanced due to significant valuation risks, which is at it should be.



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There is little point of projecting forward at an average historical rate or an average long term rate when both the market and economic cycle are at advanced levels and when individuals have clear and present liabilities on which they depend on their assets to meet

In reality the historical relationship between economic cycles and stock market valuation constrained by earnings growth and inflation can be better used to assess risks to return and an appropriate return profile.

Monte Carlo distributions

Elements of academic theory believe that movements in markets are random and that each movement is independent of prior movements. Because of this, techniques have been developed to determine the future distribution of returns and risk based on past data.

Monte Carlo techniques have been used to randomly select returns from historic data and implied data to determine the probability distribution of future return and the probability distribution of risk. While movements of the roulette wheel are indeed random and independent, market movements are not.

Since the movement of markets are relative and constrained by a number of factors, an upward movement increases the probability of a downward movement. In fact, the longer a market has been rising the greater the probability of returns towards the lower end of the normal probability distribution. In other words, depending on the trend in prices, the probability distribution will shift up towards higher returns or down towards lower returns.

The probability distribution is therefore not a constant, but a variable. Simple Monte Carlo techniques are therefore dangerous. They do not incorporate the risks of the present in their probability distributions. Market movements although unpredictable are not random and independent.

The use of Monte Carlo simulations has proceeded hand in hand with the development of simple product distribution software.

Econometric models - Vector Autoregression (VARM, VECM)

As with the above, these models suffer from the drawbacks of historic data and Monte Carlo simulations.

They also suffer from the trend inferences of multivariate regression and ignore the inverse relationship between above and below trend price movements and, the effects of permanent changes to valuation parameters.

The benefit of these models is attributed to their ability to provide correlation structures to Monte Carlo simulations. Unfortunately the techniques that provide structure to the Monte Carlo simulation also take away the economic structure. By resampling the economic data you effectively take away the important cyclical economic influences on risk and return.

One of the purported analytical benefits of VARM is that it better represents extreme events. Unfortunately, the weakness here is that extreme events may be related to different valuation and pricing structures. There is not point in structuring a portfolio now to deal with an extreme event when the risk of the event is not even in the current price.

Dynamic financial analysis/stochastic scenario generation programmes

Dynamic financial analysis represents a wide range of advanced forecasting techniques, including but not limited to VARM/VECM.

More sophisticated techniques are needed to manage business risks of insurance companies and investment banks than the individual investor. These models can become very complex, especially



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over long periods of time, but still suffer from a reliance on historical data inference and the use of Monte Carlo simulations in determining the probability distribution of uncertainty.

It is important to note the number of pension funds whose surpluses disappeared during the 2000 to 2003 bear market (?). Presumably many of these funds were using complex forecasting to assess funding risk, yet this particular scenario, one which was in the price, was not given enough weight.

Value at risk, stress testing, probability of loss

MVO constructs are deficient in managing liabilities and in structuring portfolios to meet liabilities. As a result, techniques used to manage trading risks, such value at risk, have been applied to manage and assess significant capital risks.

However, value at risk is not a suitable measure of risk for the management of long term liabilities. For one, short term price movements as defined by value at risk are not issues within long term diversified portfolios. Normal short term price movements have little relevance for longer term portfolios and are usually easily recovered over time. Significant risks, such as the liability risks discussed in TAMRIS Risk Profiling are not addressed by VAR.

Stress testing is often used to assess the risks to investment business risk and has been applied to assess the effects of significant market and economic risk on portfolios. However, there is little or no methodology for actually structuring portfolios to cope with these risks at all times. In the absence of short term liability optimisation, the only available options are to increase the low risk allocation to levels which would deprive investors of long term returns, pay expensive annual option premiums, or be constantly hedged.

With the advent of Monte Carlo and econometric forecasting models, the introduction of probability of loss/shortfall has been used as the de facto risk management tool.

Unfortunately, “probability of shortfall/loss” does not reflect the actual risk to returns. The past is not a guide to the future and random representations of the past do not represent risks to future returns.

These risk measures are not time continuums, since they do not measure risk at all stages of the investor’s lifecycle. Take any six months time frame and you will get a wide dispersion of returns from just one scenario.

TAMRIS APPROACH

The following factors differentiate TAMRIS risk/return modelling.

The modelling of low risk and equity returns is dealt with separately12 and assumptions used to structure portfolios are independent of those used to model portfolios.

Risk/return modelling is prospective, based on the risks of the current stock market and economic environment and price relatives13.

Assumptions are designed to model the ability of assets to meet needs in the event of significant stock market and economic risk. Portfolios are structured at all times to cope with extreme scenarios.

EQUITY RETURN MODELLING

12 TAMRIS constructs portfolios in liability space first and risk return space second13 It is important to note that all the information regarding risk and return is available in current market prices and relationships. Additionally, prices and relationships between prices change significantly over time. If we are truly looking at models capable of modelling returns under uncertainty, we need models to be able assess the affect of changing market relationships on portfolio structure and risk



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Modelling needs to be compatible with portfolio management objectives.

Individual investors will have limited capital, they will often be depleting capital over time. Too high a return assumption will deplete capital at a faster rate to the detriment of future financial security.

Return assumptions need to be compatible with the structure and objectives of your portfolios.

A portfolio with short term liabilities needs both a shorter term and a longer term return assumption. The first to model the short term economic and stock market risk in the market place and the second to model the longer term return profile of the portfolio.

The return assumption should be appropriate to the portfolio structure. A portfolio protected against significant short term risk within its structure can accept a longer term return assumption.

Equity return modelling requires an investment discipline.

As with investment discipline, your return assumptions should not be carried away by exuberant markets or swayed by falling ones. Importantly, all equity modelling needs to start from an investment discipline. What are you buying and what is its value?

Equity return modelling also requires an investment planning discipline.

Where assets and liabilities are being managed over time, equities will be realised continuously to meet future liabilities. As such, modelling needs to take into consideration the impact of long term portfolio activity.

VALUATION FRAMEWORK

All frameworks used to model long term equity return require an implied valuation framework, an investment rationale and a valuation discipline.

Where the service objective is the management of assets and liabilities over time, the valuation framework requires a liability perspective. It is this liability perspective which adds substance to the generation of long term equity returns.

Since the stock market is dependent on the supply of and the demand for the return on capital, the valuation framework must have both a supply side (economic) and demand a side (the market) component.

Supply side component - return on capital

All three main asset classes, cash, fixed and equities are all components of the one return, the return on capital.

The return on capital is dependent on long term economic growth. When modelling long term return, we need to model the return on economic growth.



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Equities

Fixed

Cash

Supply side risk – economic risk

Economic growth is both a long term risk and a short term risk to return. Why? Economies are markets for the demand for and the supply of goods and services. Over time imbalances between demand and supply affect two key components of stock market return, earnings and the cost of capital.

Some business cycles are influenced by demand imbalances; excess demand for goods leads to inflation. In order to manage demand interest rates rise, ultimately bringing demand back into line with supply. Others are influenced by supply imbalances; there is too much supply of goods and services to meet demand. In this instance prices either fall or capacity is reduced. The business cycle is also affected by external short sharp shocks, for example the commodity price explosion of the early 1970s and the rise in oil prices in the early 1980s.

Each type of business cycle is characterised by different risks, but both affect the level of economic activity, profits and hence share prices. Business cycles are a given. Acceptance of demand and supply imbalances within economies is key to modelling and understanding the risks to return.

At the end of the 1990s many saw the defeat of inflation as the end of the business cycle. Unfortunately, inflation is only one of many signs of excess or imbalance. Excessive consumer debt and over investment in productive capacity are signs of imbalances, themselves structural problems which will affect future return.

Within modelling, we know the current stage of the business cycle, we know that the risks of the business cycle increase the longer the cycle lasts and, we know the length and depth of previous business cycles. We do not know when the current cycle is due to end or start and we do not know the distribution of future cycles, but we know they exist, the frameworks in which they develop. They will occur in the future and they will affect return.

What we do know is that the longer the business cycle, the smaller the future rate of growth of profits and that the greater the imbalances the greater the potential decline in profits.

The decision therefore, is what type of business cycle do you want your return assumptions to be able to cope with?

The business cycle poses risks to return over short time periods, but on its own does not pose a significant risk to capital. The major risk/return factor is the market’s valuation of the return on capital.

Demand side components, equity risk premium and investor preferences

As discussed in TAMRIS Investment Discipline, the difference between the return on equity capital and the cost of capital is called the real14 “equity” risk premium. The actual risk premium the investor buys is determined by the market’s valuation of the company’s real equity risk premium.

14 This is the equity risk premium the company receives, not the investor, since the investor must pay a price for this premium.



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The demand for equities is a function of the supply of earnings, the risk to earnings and the demand for earnings. The demand for earnings is influenced by investor preferences; investors prefer greater security and certainty of earnings to uncertainty and, more earnings growth to less15.

Periods of economic uncertainty lead to lower valuations of future earnings because investors need a larger risk premium to hold stocks. Periods of economic stability lead to higher valuations of future earnings because investors require less cover against risk.

Future rIsk, historic price of earnings (P/E ratio)

Future real

return

Interest rate, inflation

expectations.

Risk free rate and,

expectations

As the above graph shows, lower equity risk premiums are associated with higher price earnings ratios and expectations of economic stability and, higher equity risk premiums with less stability and lower price earnings ratios.

The greater the risk to earnings, the larger the risk premium, the greater the potential for return. Because economies move in cycles, low risk premiums late in the cycle represent significant risks to future return.

Periods of low equity risk premiums represent periods of higher risk. When shares are no longer priced as risky assets, stocks can no longer cope with risk.

An equity investment with no risk is a risk free asset. At low risk premiums there is either no rationale for equity investment or no rationale for low risk investment.

Demand side risk – liability/valuation risk

The second risk to return is therefore valuation risk or liability risks. When modelling long term return we also need to be able to model stock market valuation. Stock markets represent both a long term risk and a short term risk to capital invested.

When valuation risk is high and the economic cycle is advanced, long term risks to return are extreme. Excess valuations occur when there is a demand/supply imbalance. Normally over valuations occur because of the inability of the supply of earnings to meet the demand for earnings and under valuations because the demand for earnings exceeds the supply.

Demand & supply

The interaction of the economic (supply side) and the market (the demand) is critical to understanding the short and long term risks to return and key to defining the return profile of the stock market.

Risks to return increase and potential return falls the more mature the economic cycle and the higher the valuation of earnings. This relationship needs to be captured with stock market and economic modelling.

15 Note the fact that markets over shoot on the upside and on the downside. The TAMRIS Consultancy

8 Algo Court, Willowdale, Ontario, Canada, M2M 3P1Telephone 416 730 8103, E mail [email protected]

http://moneymanagedproperly.com

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In certain literature high price earnings ratios are considered to be evidence of an efficient market correctly determining future growth potential. Admittedly, at the start of economic recoveries high price earnings ratios are justified by the fact that profits have room to grow.

Over the long term, there is a finite amount of economic and earnings growth. Depending on the price of the long term earnings, investment return will be above or below this long term average.

Over the short term, we are not only faced with too high or too low a price for long term growth but fluctuations in earnings and growth and, changes in the relationship between the components of capital return.

While the supply side determines the long term return on investment and constrains the movement of markets, the demand side drives the market over the short term.

THE MODEL

The following are the key components of the TAMRIS equity return model.

Time frame

A 15 year time frame is used to derive a short term return assumption and a long term return assumption.

The short time frame is based on current relative values to the end of the 15 year time frame. The long term return assumption for returns 15 years plus is based on the average cycle to cycle return assumption. For example if the end point of the short term return assumption is late economic cycle a late cycle point is used to derive the average long term return from that point on.

Shorter time frames for the short term return model can be accessed by this model. There is no need to look at year return forecasts if portfolios are structured to cope with periods of significant and stock market risk.

Business cycle

An assumed business cycle is input into the model. Over a 15 year time frame there will be between 2 and 3 business cycles. The length and range of the business cycles is derived from historical analysis of economic cycles.

While the length of the business cycle can vary TAMRIS prefers to use a 7 year model. Each business cycle comprises periods of recession, recovery, growth and economic peaks.

Shorter business cycles are normally the result of exogenous shocks to the economy. Modern economic management reacts much more quickly to economic events, partly because of the speed in which stock markets react. As a result short business cycles are less of a risk.

Longer business cycles will result in enhanced return for investors are considered a bonus to return for existing holders of equities.

The current phase of the business cycle is the starting point of the model. As the business cycle develops the phase of the business cycle within the model is adjusted. Economies at the peak of the business cycle will show a near term recession. The length and depth of the recession within the modelling can be adjusted with respect to current data.

Earnings growth

Historical patterns of earnings growth associated with the different stages of the economic cycle are input into the model. This allows price earnings ratios and dividend growth to be calculated and



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allows for valuation and modelling of the stock market cycle. As with the business cycle, near term data can be adjusted for current economic conditions.

Stock market cycle, P/E, inflation

A stock market cycle is superimposed onto the earnings and economic data. This cycle reflects the economic risks and earnings growth used in the model and moves as markets do in response to economic risk. Current market conditions, price earnings ratios and market levels are input as the starting level of the equity return modelling.

The characteristics of the stock market cycle, minimum and maximum P/Es and average price earnings ratios for different economic (business cycle, inflation) are input. Importantly the range of valuations will determine the stability of return forecasts.

For example, markets are extremely high. Within the model you can impose a move back to a more sensible valuation range, meaning that your return forecasts are not based on current market expectations but more conservative one. Again, this represents the difference between the planning and the investment strategy.

The two most important variables are a) the price earnings ratios applicable to different market and economic conditions and b) the end P/E and price relatives. Taking a conservative P/E range will reduce the risk of return assumptions being incorrect while the end P/E ratio is critical to the overall conservatism of the return assumptions.

Importantly the range of values that a 15 year stock market cycle provides also helps risk reduction. For one, the return assumption is not based on sale at market peaks, nor is it based on sale at market troughs. There is a wide range. As you will be looking to sell a fair to high market value, the modelling does incorporate all the market averages.

Taxation, costs etc

The costs to return also need to be modelled. Management expense ratios for mutual funds, taxation all impact on return over time.

Modelling in practise

The size and timing of future economic cycles are not going to be the same as those input into your models. Your models are there to ensure that the risks to return of economic cycles and stock market corrections, bear markets etc are taken into consideration in the return estimation.

The objective is to ensure that there is a stable platform in which your investment planning can operate within uncertainty. Investors are looking for stability and certainty in ability to meet planned financial commitments. The return modelling you employ is critical in providing this.

As stated, the model is based on the present moment in time. At high market levels, the model discounts a major correction or crash, at peak economic cycles the model discounts a recession. Clients’ needs are not based on optimistic assumptions.



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S&P 500 - Total real return after charges - May 2000 to May 2014

100

71.7

52.7

44.3

52.6

70.8 70.173.4

82.0

71.1

58.6

71.9

79.4 81.6 83.6

0

20

40

60

80

100

120

03-May-00 May-01 May-02 May-03 May-04 May-05 May-06 May-07 May-08 May-09 May-10 May-11 Apr-12 Apr-13 Apr-14

Total real return after charges 16

The above chart shows the real long term risk/return assumption for the US market (S&P 500) as of May 2000.

Investors using this type of model would not have had to readjust long term expenditure in the light of market falls during the ensuing bear market.

Because rising markets = falling future returns and falling markets = rising future returns, short term market movements have minimal affect on the ability of assets to enhance or detract from financial security.

Only sustained above average investment returns supported by valuations can be used to enhance long term financial security.

The benefit of this approach is that stress testing has already hit future return with stock market and economic risk enhancing long term income and capital security.

It is important to note that the modelling of the US market economic cycle was kept at kept at a cyclical peak for some two years before the bear market. While the cycle continued to mature and markets continued to move higher, the model merely discounted further falls in the market.

Model insights

It is important to note how much risk and return depends on short and long term economic conditions. Diversifying globally is important.

The modelling framework used by TAMRIS is applied to all markets to which you are allocating. While this means quite rightly that you need economic analysis of all economies, it does mean that all return assumptions used are related to global stock market and economic cycles. As such, both market, economic and intra market relationships are all correlated. This is something which conventional modelling does not achieve.

Secondly, because you are not attempting to forecast the size and timing of both markets and economies, something which institutions spend a lot of time and money developing, you need fewer analytical resources.

LOW RISK RETURN MODELLING

16 This is the long term real return after mutual fund management expenses and income tax on dividends. Asset/liability modelling would also hit returns for portfolio management and investment planning fees/expenses.



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Low risk portfolios structured around short term financial needs are designed to manage short term financial security in the event of significant stock market and economic risk.

Their primary objective is the delivery of financial security. As such, these portfolios are intrinsically different from low risk portfolios run primarily in risk/return space alone.

It is important to note that the risks applying to a low risk portfolio that is not directly used to be meet liabilities is less than one which is, but the risks of a portfolio not structured to meet liabilities but nevertheless has to, are higher than risks of one which is structured.

Before we look at the risks and the role of the risk assumptions in modelling, we need to understand the structure of the low risk portfolio and its objectives over time.

LOW RISK STRUCTURE

Portfolios derived from asset and liability management frameworks are structured to meet income and capital needs in the form of interest from low risk assets and dividends from equities and capital from low risk assets.

1. Interest and dividends from the portfolio are added to cash balances which are used to meet immediate income and capital expenditure.

2. Fixed interest investments mature to provide future capital in time to meet the client’s short term liability profile.

3. Equities are sold periodically at fair to high market valuations to replenish low risk capital, if needed.

The structure of a low risk portfolio is therefore cash, to meet immediate needs and, low risk securities to meet future needs.

The simplest structure is to merely divide the low risk portfolio between cash and government fixed interest securities. As securities mature, realised equity capital is used to purchase new securities as determined by the short term allocation profile and current recommended allocation and securities.

However, allocating purely to government fixed interest securities will often miss out on higher yielding opportunities within corporate investments and risk/return benefits of global diversification. The longer end of the low risk portfolio is therefore often used to allocate to higher return/higher risk lower risk investments.

It is important to note that, apart from sales and purchases at the margin, during most market conditions these higher risk low risk assets need not need to be touched. It is the dedicated components of the portfolio which will be recycled over time and rebalanced by capital realised from equities.

It is important within low risk management to keep costs down and active management of dedicated fixed interest components would not normally be advocated. Because the movement of fixed interest investments are largely homogenous, the only way to beat the market is by taking different views than the market on interest rate movements.

The longer term low risk components of portfolios derived from asset and liability management frameworks are structured to only be needed through a full inflation and interest rate cycle.



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Risk

Time frame

Quasi cash matched/ managed

Cash matched

Cash



The longer term component is the quasi cash matched component as shown in the above chart. Importantly, during periods of extreme stock market valuation risk, low risk allocation will often be pushed out further.

As such, short term “dedicated” low risk portfolios are normally distributed between cash, cash matched and quasi cash matched/ managed components.

Within this context, the objectives, framework and limitations of low risk return modelling need to be clearly defined.

Short term asset liability modelling and management principles

Assumptions used to determine low risk investment strategy and security selection should be different from those used to determine asset liability modelling.

The objectives of low risk portfolio management are different for the management of liabilities and risk and return than the management of risk and return.

The short term is the most important portfolio component. The objective of low risk asset liability modelling should be to provide income and capital security irrespective of the economic and stock market conditions.

The objective of low risk return modelling is not to predict return but to model the ability of assets to provide income and capital security for a given level of risk. Portfolios are structured at all times to cope with extreme scenarios.

Return assumptions are not constant and, prices and the financial environment change.

Traditional fixed interest management engineers return by one of two methods.

The first is to lower the quality of the bonds held, holding corporate bonds, undated securities and moving overseas.

The second is to anticipate changes in the economic environment, especially changes in inflation and interest rates.

The problem with the above is that a liability objective restricts the duration and the quality of the investments. Additionally, whereas a general fund can be used to meet risk/return objectives, a personal approach is needed to meet asset/liability objectives. There are thousands of different portfolios.

In fact, you still only manage the allocation strategy and security selection from one central point, investment planning decision rules distribute and personal the allocations and security selection.

Where a personal approach is taken, the costs and the logistics of trading fixed interest investments are prohibitive. Low risk portfolios used to meet liabilities are more often than not passive/dynamic portfolios.



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Whereas the active fixed interest portfolio may be constructed on an optimisation of risk and return, the liability portfolio is optimised in accordance with the liability profile first and risk return second. Because you are modelling liabilities and assets the asset liability modelling becomes a management tool.

THE MODELLING OF RISK

The nominal returns and capital values of low risk investments are easy to forecast. The risks are not and, it is the modelling and management of risk that short term asset liability modelling and management is primarily concerned with.

Why? An individual’s financial security depends on their low risk assets to meet their financial needs as and when they arise and to protect their assets and financial security in the event of significant stock market and economic risk.

The risks that affect the modelling and management of low risk investment are divided intro three main areas

Asset risks such as inflation, interest rate, liquidity, credit and currency risks

Liability risks in the context that liabilities or expenditure is greater than planned.

Stock market and economic risk in the sense that the actual period of stock market and economic risk will vary.

ASSET RISKS

The main risks that need to be factored into low risk return modelling are as follows.

Inflation and interest rate risk

Inflation risk affects the real capital value of securities at maturity and the interest distributed. If inflation forecasts are incorrect there will be a shortfall.

The objective here is not to enter the inflation you think will occur, but the inflation risk you want to cover. Note, that inflation risk is also effectively interest rate risk.

Interest rate risk, often a direct affect of rising inflation (cash is exposed to falling interest rates) affects those investments where the maturity of the low risk asset is longer than the maturity of the liability. Many corporate bond investments are undated as are effectively mutual fund portfolios of fixed interest securities.

Liquidity risk

A 7 year bond held to maturity has no liquidity risk, yet a 7 year bond sold prior to maturity does. Liquidity risk is the potential loss of capital on realisation and with properly structured low risk portfolios this should only apply to undated investments.

Undated “corporates” and mutual fund holdings of fixed interest securities need to be hit with liquidity risk.

Credit risks

Corporate bonds, high yield bonds and emerging market debt are exposed to credit risk, which is the risk of default. Many apparently financially secure companies have gone to the wall. Corporate bond investments should have credit risk adjustment factored into the valuation.



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Currency risks

International holdings are exposed to currency risk. Under and overvaluation of currencies needs to be factored into all international bond holdings.

Redemption risks

A basic for fixed term investments; bonds priced above par will see prices fall towards par values the closer you move to maturity. This risk is not so obvious within undated investments or collective investments. Corporate undated issues where the yield quoted is below the coupon are exposed to loss on capital on redemption and this needs to be factored into modelling assumptions.

Other risks

There are many specialist low risk investment vehicles where the actual risks are not evident. Many of these assets are often sold as lower risk investments but are in fact quasi equity investments or linked to the value of some other real asset.

The above risks represent the main modelling risks. How you tune your risks will determine the ability to handle risk and the ability to manage return.

More conservative assumptions will provide a greater level of security than that modelled and will provide effectively greater income and capital security.

The application of the different risks to the overall portfolio is an asset management and an investment planning exercise. The last thing you want is for the planned income and capital security to fall short.

Liability risks

Investors often underestimate the income and capital expenditure they will often need. It is sometimes prudent to adjust short term liabilities up by a small factor to allow for variance in planned expenditure, especially at the start of the client relationship. This is an investment planning exercise.

Note that the liability modelling should cover the risks to nominal income streams and the risks to future liabilities. Some liabilities increase at a rate in excess of the rate of inflation.

The management of short term risk

This is the risk to the ability of assets to meet liabilities in the event of extreme economic and stock market conditions. This is the management of the short term allocation within portfolios. During periods of significant risk, the minimum level of cover also needs to be managed as low risk capital is drawn down to avoid forced sales of equities.

LONG TERM ASSET LIABILITY MODELLING

The long term asset liability model integrates the short term asset/liability model with the equity allocation and long term return assumptions

This model assesses the ability of an investor’s portfolio to meet short and long term financial needs in the face of significant financial and economic risk.

Is there enough capital to support lifetime income and capital expenditure?

Can he or she meet school fees, retire at 60, purchase a holiday home, leave capital to children.



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If not, what course of action should be taken?

Why long term asset liability modeling important? Future assets and needs impact on current strategy and planning. There could well be an imbalance within the ability of current assets to meet future needs which planning and appropriate strategy can remedy. But this needs to be done in advance.

For example, most high net worth investors will actually be depleting capital over time17. Long term asset liability modeling is key to the management of this capital depletion.

This is also a framework against which an investor can make realistic investment and lifestyle decisions and is part of the interactive investment planning process. It is, in fact, the investor through his or her objectives and risk aversions that selects the portfolio.

Investment planning

The long term asset liability modeling function moves from a portfolio construction role to an investment planning framework governing the management of personal financial assets and needs over time.

The importance of this may not be immediately obvious. Conventional asset liability modeling is either too complex or too simple to allow planning and structure of assets to go hand in hand.

While the investment planner is able to focus on the management of needs over time the asset liability modeling, integrated with asset management, is able to make all the necessary adjustments to portfolio structure and strategy.

Key to long term asset liability modeling is therefore to allow planners to adjust the allocation and management of future inflows and outflows of portfolio capital.

Long term modelling assumptions

There are three considerations that need to be addressed within long term return modeling assumptions.

17 It is only the mega net worth client who will not be depleting capital. Modelling should also be carried out in real terms, not nominal terms. In nominal terms there may well be no depletion of capital for many clients.



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(50,000)

-

50,000

100,000

150,000

200,000

250,000

300,000

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

2049

2051

2053

Years

Cap

ital

at

sta

rt o

f e

ach

ye

ar

Cash & bonds Equities

Problems Problems anticipated anticipated in advancein advance

Now I know Now I know what you are doing what you are doing

and,and,why I need you... why I need you...



The first is the changing structure of the portfolio over time in response to the changing liability profiles. A simple short term optimization algorithm within the long term model ensures that at all times the portfolio structure reflects the liability profile.

The second is that equity portfolio allocation needs to be adjusted back to the benchmark allocation otherwise higher return assumption components will exaggerate portfolio return as allocation increases.

The third is the long term low risk return assumptions. There should be a constant long term real return assumption for low risk assets.

WHY ASSET LIABILITY MODELLING?

Without the ability to structure assets to meet liabilities you cannot personalise portfolios to client liability profiles.

Without the ability to adjust portfolio structure for inflows and outflows of income and capital to the portfolio you cannot perform long term investment planning, crucial to the delivery of total asset, life cycle wealth management.

Without a liability model you cannot structure a portfolio to secure income and capital needs in the event of significant stock market and economic risk.

Because portfolio structure is linked to liability profiles, asset management can be integrated with the management of liabilities.

Because risk/return assumptions adjust as markets and prices change, the asset liability modelling is dynamic and adjusts structure and planning automatically.

If asset management is integrated with a dynamic valuation, allocation and management framework, asset/liability modelling and management provides a dynamic portfolio management and investment panning framework.

Without this framework the delivery of personalisation is a complex, costly and unmanageable process.



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