Dynamic Core-Satellite Strategy

Dynamic Core-Satellite Strategy

The approach of the Dynamic Core-Satellite (DSC) risk-budgeting methodology, is essentially the separation of Assets into two components.

 

A.    Partly a Core portfolio component, which seek to track a designated benchmark

B.     and partly a more risky Performance oriented component, made up of one or more Satellite portfolios with an absolute return mandate.

 

The approach is useful both in the context of relative Asset Liability Management, where the aim may be to neutralize Liabilities, and hence is an extension of Liability Driven Investing (LDI). Or 

Absolute return strategies, where the DSC approach can be used smoothen the ride, and limit drawdowns.

In both cases the Asset Allocation inside does not change, they are so to speak independent of whether the objective is relative or absolute return. The Core portfolio does however change, as it is suppose to mimic the Benchmark as close as possible. In a relative return LDI context, where the objective is to hedge some future Liabilities like Inflation, the Core portfolio is named the “Liability Hedge Portfolio” and is allocated to Assets with a high correlation to Consumer Price Index. Where as in a more absolute return context the Core may be composed of a risk-return optimized portfolio of Beta exposure.

Sometimes we are operating with two extra types of portfolios namely:

A.    Completeness portfolios, used tactically to port exposure to or from the combined portfolio, for example in case the Country, Sector or Style exposure deviate unwanted from the Strategic Allocation.

B.     A Extreme Risk portfolio, designed to hedge unwanted more regime dependent risks.

Although the weights allocated to the Core and the Satellite can be static, the next step is to let the Satellite allocation be dynamic depending on the current cumulative outperformance of the combined portfolio. There are several options.

In the Constant Proportion Portfolio Insurance (CPPI) variant, total assets are dynamically allocated to Satellite Performance seeking portfolios in proportion to a Multiple of a designated portfolio Cushion. The Cushion being defined the difference between the value of the combined over-all portfolio, and a desired protective floor (For example 90% of resent top), and the Multiple being a numeric value acting so to speak, as a risk gas-pedal between the two portfolios, the higher its numeric value, the higher allocation to the Performance portfolio.

In the Variable Proportion Portfolio Insurance (VPPI) variant the Multiplier is also dynamic and can depend on a whole range on input like P/L, gap-risk, Volatility and Manager views.

Under such allocation strategy the portfolio’s exposure to the risky Satellite portfolio tends to zero as the cushion approaches zero; when the cushion is zero, the portfolio is completely invested in the Core portfolio. Thus, in theory the strategy ensures that the portfolio never falls below the floor; in the event that it touches the floor, the fund is “dead”; that is, it can deliver no performance beyond the guarantee.

The approach should be seen as a structured and hence sustainable form of portfolio management, that allows an investor to effectively truncate the relative-return distribution in such a way as to allocate the probability weights away from severe relative underperformance and towards greater potential outperformance.

In the context of absolute return strategies, it is possible to propose a trade-off between the performance of the Core and Performance Satellite. This trade-off will not be symmetric, as it involves maximizing the investment in the Performance Satellite when it is outperforming the Core and, conversely, minimizing allocation to Performance Satellite it when it is underperforming. Like standard CPPI, this dynamic allocation technique requires that a floor of potential under-performance is established, and is usually set as a fraction of the benchmark portfolio, say 90%. Investment in the Performance Satellite then provides access to potential outperformance of the benchmark.

This on-going process leads to an accumulation of past outperformance and results in an increase in the cushion and hence in the potential for a more aggressive allocation in the future. If the Performance Satellite has underperformed the benchmark, however, the fraction invested in in it, decreases in an attempt to ensure that the relative performance objective will be met.

How do we determine allocation to each portfolio?

Let Pt be the value of the portfolio at date t. The portfolio Pt can then be broken down into a floor Ft and a cushion Ct, according to the relation Pt = Ft + Ct. Let Bt be the benchmark.

The floor is given by Ft = kBt, where k is a constant Multiplier less than one.

Finally, let the investment in the satellite be Et =wSt =mCt =m(Pt -Ft), with m being a constant multiplier greater than 1 and w the fraction invested in the satellite. w being the fraction invested in the satellite. The remainder of the portfolio, Pt - Et = (1-w)Bt, is invested in the benchmark.

Extensions to Dynamic Core-Satellite Analysis

Setting the floor is the key to dynamic core-satellite management, since it ensures asymmetric risk management of the overall portfolio. If the difference between the floor and the total portfolio value increases, that is, if the cushion becomes larger, more of the assets are allocated to the risky satellite. By contrast, if the cushion becomes smaller, investment in the satellite decreases.

In the standard case presented above, the floor is a constant fraction of the benchmark value Ft = kBt. However, depending on the investment purpose, different floors might be used to exploit the benefits of core-satellite management. Indeed, the core-satellite approach can be extended in a number of directions, allowing the introduction of more complex floors or of so-called investment goals. Instead of imposing a lower limit on total portfolio value, a goal (or cap) restricts the upside potential of the portfolio. It can also be extended to account for a state- dependent risk budget, as opposed to the constant expenditure of the risk budget implied by the basic dynamic core-satellite strategy. We list below several possible floor designs, and we then discuss the option of making a goal part of the investment process. 

Conventional strategies consider the floor but ignore investment goals. Goal-directed strategies recognize that an investor might have no additional utility gain once a total wealth Gt beyond a given goal is reached. This goal, or investment cap, may be constant; it may also be a deterministic or stochastic function of time. Goal-directed strategies involve optimal switching at some suitably defined threshold above which hope becomes fear (Browne 2000).

It is may not be immediately clear why any investor would want to impose a strict limit on upside potential. But the intuition is that investor may prefer forgoing additional performance beyond a certain threshold, where the relative utility of greater wealth is lower, and rather benefit from a reduced probability and cost of downside protection. In other words, without a performance cap, investors run a higher risk of missing a nearly attained investment goal.

Such a goal can be accommodated by a strategy in which the fraction invested in the performance-seeking satellite is a multiple m2 of the distance to the goal, whereas a floor can be accommodated by a strategy in which the fraction invested in the performance-seeking satellite is a multiple m1 of the distance to the floor. If, in addition, one defines the threshold wealth (denoted by Tt) at which the investor shifts from a goal-oriented focus to a risk-management focus, one obtains a funding state-dependent dynamic allocation strategy, with the threshold Tt to ensure a smooth transition. 

Capital guarantee floor            

This is the most basic expression of a risk budget given by Ft = ke-r(T-t)A0, where r is the risk-free rate (here assumed to be a constant), k a constant <1, and A0 the initial amount of wealth. The capital guarantee floor is what is usually used in CPPI.

Benchmark protection floor            

This is the basic dynamic core-satellite structure; it protects k% of the value of any given stochastic benchmark: Ft = kBt. In asset management, the benchmark can be any given target (e.g., a stock index). In asset/ liability management, the benchmark will be given by the liability value, so At ≥ Ft = kBt is a minimum funding ratio constraint.

Maximum drawdown floor            

Extensions of the standard dynamic asset allocation strategy can accommodate various forms of time-varying multipliers and floors. Forexample, consider a “drawdown constraint” that requires the asset value At at all times to satisfy At > αMt, where Mt is the maximum asset value reached between date 0 and date t: max(As)s<t. In other words, only portfolios that never fall below 100α% of their maximum-to-date value are admitted, for some given constant α. The interpretation is that any drawdown must always be smaller than 1-α. These strategies were introduced by Estep and Kritzman (1988), who labeled them “time invariant portfolio protection strategies” (TIPP), and later formalized by Grossman and Zhou (1993) and Cvitanic and Karatzas (1995). This maximum drawdown floor was originally described for absolute risk management, but by taking At/Bt > α max(As/Bs) s<t, where Bt is the value of any benchmark, it can also be used for relative risk management.

Trailing performance floor            

This floor prevents a portfolio from posting negative performance over a twelve-month trailing horizon, regardless of the performance of equity markets. More formally, it is given by Ft = At-12, where At-12 is the portfolio value twelve months earlier. Again, by taking Ft = Bt-12, for example, this constraint can be extended to relative risk budgets.