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 The Importance of Asset Liability Matching (ALM)

By Sharath Sury

Posted On: About Sharath Sury - News and Events at 3/11/2010 4:33 PM

In recent years, investment, portfolio, and endowment managers have become acutely attuned to the risks of unmatched cash flows in their portfolios. In particular, the ability of assets to generate sufficient cash flows to meet liability funding obligations has waned in the past decade.

As a result, new emphasis has been placed upon so-called "asset liability matching." In this brief, we'll examine one example of the ALM technique. Consider the case of an investment manager that has a series of assets that must fund a series of liabilities. For simplicity, we will assume that the assets consist of fixed income investments with no default risk and the liabilities consist of a known set of payouts to be made in the future.

In any analysis of fixed income (or liabilities), it is important to measure the "duration" of the income (or liability) stream. Duration is the cash-flow adjusted effective term to maturity for a series of given cash flows. Thus, duration is also sometimes referred to as "effective maturity." It is also important to know that the duration of a portfolio of assets (or liabilities) is equal to the weighted average duration of the underlying assets (or liabilities). In addition, the higher the duration of a series of cash flows, the greater its sensitivity to a change in interest rates.

There are a variety of ways to calculate duration that are beyond the scope of this brief; however, most involve either a simple calculation heuristic or an automated spreadsheet. Either way, there is a very important benefit of knowing the duration of a series of cash flows. A bondholder that holds (and reinvests the coupons of) a bond to its duration is effectively "immunized" from interest rate changes and should experience a holding period yield (HPY) that is approximately equivalent to the original yield to maturity on the bond.

Of course, there are several assumptions (e.g., the bond issuer does not default, etc). However, in the base case, this notion can be very valuable to an investment manager that needs a certain cash flow at a certain date and time. This is often the case for endowments that have payout requirements or pensions that have retirement obligations.

If an investment manager can calculate the duration of its assets and the duration of its liabilities, it can make a determination as to the interest rate sensitivity of the portfolio; and thus estimate its ability to meet its future obligations. For example, if the duration of the portfolio assets is greater than the duration of the portfolio liabilities, then the portfolio structure is susceptible to rising interest rates.

This is because the higher duration assets are more sensitive to interest rates than the lower duration liabilities. Should interest rates rise, the assets will decline in value more quickly than the liabilities will. If interest rates remain at that level, there may be a shortfall in funding the liabilities.

One way to mitigate this problem is to rebalance the asset portfolio such that the duration of the assets is equal to the duration of the liabilities, such that any interest rate change has a negligible effect. If, in the case above, the asset portfolio duration is too high, the duration must be reduced.

This reduction may be accomplished by either rebalancing the portfolio with shorter duration assets (e.g., shorter term Treasuries or even cash) or by shorting longer duration assets. The zero coupon market in Treasuries (STRIPS) is often used due to the unique result that zero coupon bonds have durations that are exactly equivalent to their maturities.

When the duration of the portfolio of assets and the portfolio of liabilities is equivalent, changes in interest rates should have a negligible effect on the structure: the portfolio is said to be duration matched. This is a prime example of the benefit of ALM.

Of course, there are risks other than changing interest rates. Furthermore, duration itself is not static, and portfolio rebalancing must be dynamic to account for such changes. However, in principle, this form of ALM can work to help investment managers put some control on at least one form of risk in our ever-more complex investment world.

Risk Measurement - A Multi-Dimensional Concept!

By Sharath Sury

For the past 50 years, many financial economists and professional investment managers relied upon the concept of "mean variance optimization (MVO)" to design investment portfolios. The principle was simple: maximize mean return subject to a certain variance (risk); or minimize variance subject to a certain mean return.

While the MVO technique is well-intentioned, it suffers from an incomplete definition of risk. It has long been known that asset classes can be described using various degrees of specificity, sometimes known as "moments of the distribution." For example, the first moment of the return distribution of an asset class is its mean return. The second moment is known as variance. For a normally distributed asset class with well behaved properties, a variety of analytical techniques can be applied and conclusions drawn based upon these two moments alone (mean and variance).

Unfortunately, many asset classes do not behave according to what the normal distribution would suggest. Further, asset classes that exhibit "normality" for one time period may not exhibit it during other time periods. The incorporation of alternative asset classes (e.g., hedge funds, real estate, private equity) increases the likelihood that an overall portfolio will exhibit degrees of non-normality.

As a result, it is often important to consider at least two other "higher order" moments of the return distribution. The third moment is known as "skew" or asymmetry. It reflects the degree to which an asset class may have a higher proportion of negative (or positive) returns. Some hedge fund strategies are built and marketed explicitly on this notion: while they may exhibit low levels of variance (or standard deviation), they concomitantly exhibit high levels of negative skew (or returns which are asymmetrically biased to the downside).

The fourth moment of a return distribution is referred to as "kurtosis," or more colloquially as "fat tails." Kurtosis reflects the degree to which the return distribution may be subject to extreme events. Thus, the "fat tails" refer to the graphic representation of the returns as exhibiting a higher probability of extreme results than the normal distribution would suggest. As an example, higher levels of geopolitical uncertainty can increase the kurtosis of particular asset classes.

Thus, investment managers and asset allocators who rely solely upon the popularly employed MVO techniques may be missing key risk factors that can adversely affect a portfolio and may therefore provide solutions that are incomplete with respect to skew and kurtosis.

New methods for dealing with the shortcomings of MVO include the use of other constraints in the optimization. For example, the so-called "mean-conditional value at risk (MCVaR)" is a methodology which seeks to incorporate skew and kurtosis. CVaR essentially relates to the "area of the return profile" below which an investor is "at risk."

By maximizing mean return subject to an acceptable level of CVaR, an investment manager may be better able to capture the important risks of the portfolio. Recent (and repeated) empirical research has shown that-in hedge fund portfolios alone-the MVO methodology has underestimated the amount of risk (as defined by CVaR) by as much as 50%!

In the past few years, CVaR has been joined by other risk measures, such as "Omega," to help broaden the traditional definition of risk beyond the confines of simple variance or standard deviation. In the end, more complete definitions of risk should lead to more robust portfolio optimization solutions.