Risk Control through Dynamic Core-Satellite Portfolios of ETFs: Applications to Absolute Return Funds and Tactical Asset Allocation

Transcription

1 An EDHEC-Risk Institute Publication Risk Control through Dynamic Core-Satellite Portfolios of ETFs: Applications to Absolute Return Funds and Tactical Asset Allocation January 2010 with the support of Institute

2 This research received the support of the research chair on Core-Satellite and ETF Investment sponsored by Amundi ETF. 2 Printed in France, January Copyright EDHEC The opinions expressed in this study are those of the authors and do not necessarily reflect those of EDHEC Business School. The authors can be contacted at research@edhec-risk.com.

3 Table of Contents Abstract...5 Introduction Method: Dynamic Risk Budgeting Beyond Diversification: Absolute Return Funds of ETFs Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework Conclusion...27 References...29 About EDHEC-Risk Institute...31 About Amundi ETF...35 EDHEC-Risk Institute Publications and Position Papers ( )...37 An EDHEC-Risk Institute Publication 3

4 About the Authors Noël Amenc is professor of finance and director of EDHEC-Risk Institute. He has a masters in economics and a PhD in finance and has conducted active research in the fields of quantitative equity management, portfolio performance analysis, and active asset allocation, resulting in numerous academic and practitioner articles and books. He is a member of the editorial board of the Journal of Portfolio Management, associate editor of the Journal of Alternative Investments and a member of the scientific advisory council of the AMF (French financial regulatory authority). Felix Goltz is head of applied research at EDHEC-Risk Institute. He does research in empirical finance and asset allocation, with a focus on alternative investments and indexing strategies. His work has appeared in various international academic and practitioner journals and handbooks. He obtained a PhD in finance from the University of Nice Sophia-Antipolis after studying economics and business administration at the University of Bayreuth and EDHEC Business School. Adina Grigoriu is head of asset allocation at AM International Consulting, where she advises asset managers on constructing their hedge fund of fund portfolios as well as dynamic core-satellite portfolios. She has an actuarial degree and extensive experience in finance, including quantitative modelling. She started her career as a derivatives trader. She then joined a multinational asset management company where she held several positions, ranging from product manager to fund manager and head of ALM. 4 An EDHEC-Risk Institute Publication

5 Abstract An EDHEC-Risk Institute Publication 5

6 Abstract Asset managers generally focus on diversification or returns prediction to create added value in portfolios of exchange-traded funds (ETFs). This paper draws on dynamic risk-budgeting techniques to emphasise the importance of risk management when decisions to allocate to ETFs are made. Absolute return funds, in which the low-risk profiles of government bond ETFs and conditional allocations to riskier equity ETFs can be combined to obtain portfolios that beyond the natural diversification between stocks and bonds provide upside potential while protecting investors from downside risk, are an initial application of ETFs to allocation decisions. A second application is risk control of tactical strategies. Dynamic risk budgeting is used to provide risk-controlled exposure taking the manager s forecasts as a given to an asset class. This paper shows that, even if the manager is an excellent forecaster, this approach yields intra-horizon and end-ofhorizon risk-control benefits considerably greater than those of standard tactical asset allocation. 6 An EDHEC-Risk Institute Publication

7 2. Implementing Introduction Efficient Indexation An EDHEC-Risk Institute Publication 7

8 Introduction This paper examines the ways dynamic asset allocation techniques can be used to manage portfolios of exchange-traded funds (ETFs). First, dynamic allocation to stock and bond ETFs and traditional static diversification are compared. Second, tactical allocation to stock and bond ETFs and risk-controlled allocation with both forms of allocation informed by the same return forecasts are compared. The paper shows that dynamic asset allocation techniques that can be used with frequently traded and broadly diversified instruments such as ETFs make it possible better to address investor concerns over drawdown and intra-horizon risk, whether or not the manager wishes to make return predictions. The asset management industry has traditionally focused largely on security selection. Following the evidence of the importance of asset allocation (Brinson et al. 1986), the industry has paid increasing attention to passive investment vehicles that provide exposure to broadly diversified baskets of securities. Such vehicles make security selection unnecessary and allow asset managers to concentrate on allocation to different asset classes or styles. Asset managers have two main means of using asset allocation to add value. The first is strategic asset allocation, in which the goal is to diversify the asset mix so as to obtain the best possible risk/return tradeoff for investors. Strategic allocation depends mainly on the correlation of the returns of different asset classes and on the risk premia of these asset classes. The challenge is to estimate these parameters. In addition, correlations and risk premia are not necessarily stable. In particular, diversification often fails when it is most needed, as correlation increases during crises (Longin and Solnik 2001). The second means is tactical allocation, which relies on predicting the short-term returns on different asset classes. Managers can then increase exposure to high-return asset classes and decrease exposure to low-return asset classes. The primary aim of tactical allocation is often outperformance rather than risk management. Asset managers are relying more and more on ETFs to implement these strategies. The volume of assets invested in these funds has increased more than five-fold in the past six years, both in Europe and in the United States (Deutsche Bank 2009). Miffre (2007) and Hlawitschka and Tucker (2008) empirically assess the potential diversification afforded by holding more than one ETF. Amenc et al. (2003) and de Freitas and Barker (2006) analyse tactical allocations to ETFs on different asset classes and styles. The objective of this paper is to analyse portfolios of ETFs that go beyond these traditional diversification and tactical allocation concepts. Rather than focusing on diversification alone, we apply dynamic risk management techniques that take into account investors aversion to intrahorizon risk. After all, investors are averse not just to end-of-horizon risk but also to negative outcomes within the investment time period (Kritzman and Rich 2002; Bakshi and Panayotovb 2009). Addressing these concerns requires dynamic risk management. We first analyse how this concept can be used when, in the absence of any views on the returns to these asset classes, decisions to allocate to stocks and 8 An EDHEC-Risk Institute Publication

9 Introduction to bonds are made. We describe a dynamic risk management technique that makes it possible to provide relatively smooth returns with limited risk, an outcome similar to that sought by the absolute return funds that have proliferated in recent years. We then introduce a novel means of using forecasts of asset class returns to construct dynamic portfolios of stock and bond ETFs. Rather than using a strategy in which asset class weights depend only on return predictions, we take the dynamic core-satellite approach to act on return predictions the dynamic risk budget is a given. The aim of the approach is to provide an element of risk control. Expected outperformance of an asset class does not lead directly to changes in weights. Instead, we adjust the multiplier in the dynamic strategy in keeping with the predicted outperformance, thus changing the weights indirectly. We show that, even if the manager is an excellent forecaster, this approach yields risk-control benefits considerably greater than those of standard tactical asset allocation. The paper proceeds as follows. Section one describes the method we apply to the management of portfolios of ETFs. Section two discusses the application to the management of absolute returns and section three considers tactical bets. A final section provides conclusions. An EDHEC-Risk Institute Publication 9

10 Introduction 10 An EDHEC-Risk Institute Publication

11 2. 1. Implementing Method: Dynamic Efficient Risk Indexation Budgeting An EDHEC-Risk Institute Publication 11

12 1. Method: Dynamic Risk Budgeting In the remainder of this paper, we draw on the core-satellite approach to make allocations to ETFs. This section first introduces the basic dynamic core-satellite approach and then discusses possible extensions Dynamic Core-Satellite Portfolio Choice The core-satellite approach divides the portfolio into a core component, which fully replicates the investor s designated benchmark, and a performance-seeking component, made up of one or more satellites, which is allowed higher tracking error. Although the weights allocated to the core and to the satellite can be static, the proportion invested in the performanceseeking portfolio (the satellite) can also fluctuate as a function of the current cumulative outperformance of the overall portfolio, thus making the approach dynamic. The dynamic core-satellite concept builds on constant proportion portfolio insurance (CPPI). This principle, described by Black and Jones (1987) and Black and Perold (1992), allows the production of option-like positions through systematic trading rules. CPPI dynamically allocates total assets to a risky asset in proportion to a multiple of a cushion defined as the difference between current portfolio value and a desired protective floor. The effect is similar to that of owning a put option. In CPPI, the portfolio s exposure tends to zero as the cushion approaches zero; when the cushion is zero, the portfolio is completely invested in cash. So, in theory, the guarantee is perfect: the strategy ensures that the portfolio never falls below the floor; in the event that it touches the floor, the fund is dead, i.e., it can deliver no performance beyond the guarantee. This CPPI procedure can be transferred to a relative return context. Amenc, Malaise, and Martellini (2004) show that an approach similar to standard CPPI can be taken to offer the investor a relative performance guarantee (underperformance of the benchmark is capped). Conventional CPPI techniques still apply, as long as the risky asset is re-interpreted as the satellite portfolio, which contains risk relative to the benchmark, and the risk-free asset is re-interpreted as the core portfolio, which contains no risk relative to the benchmark. The key difference from CPPI is that the core or benchmark portfolio can itself be risky. In a relative-risk context, the dynamic core-satellite investment can be used to improve the performance of a broad equity portfolio by adding riskier asset classes to the satellite. Dynamic core-satellite investing may also be of interest to pension funds, which must manage their liabilities: the core then is made up of a liability-hedging portfolio, and the satellite is expected to deliver outperformance. This dynamic version of a core-satellite approach, which can be seen as a structured form of portfolio management, is hence a natural extension of CPPI techniques. The advantage is that it allows an investor to truncate the relative return distribution so as to allocate the probability weights away from severe relative underperformance and towards greater potential outperformance. Core-satellite portfolios are usually constructed by putting assets that are supposed to outperform the core in the 12 An EDHEC-Risk Institute Publication

13 1. Method: Dynamic Risk Budgeting satellite. But if economic conditions become temporarily unfavourable the satellite may in fact underperform the core. The dynamic core-satellite approach makes it possible to reduce a satellite s impact on performance during a period of relative underperformance, while maximising the benefits of the periods of outperformance. As it happens, investor expectations are rarely symmetric. In other words, when stock market indices perform well, investors are happy to be engaged in relative return strategies. On the other hand, when stock market indices perform poorly, they express a strong desire for absolute return strategies. Value-at-Risk minimisation and volatility minimisation allow only symmetric risk management. For example, the minimumvariance process leads to lost upside potential in the performance of commercial indices in exchange for lower exposure to downside risk. Although this strategy allows long-term outperformance, it can lead to significant short-term underperformance. It is also very hard to recover from severe market drawdowns. The dynamic core-satellite technique, by contrast, focuses on asymmetric risk management. From an absolute return perspective, it is possible to propose a tradeoff between the performance of the core and satellite. This trade off is not symmetric, as it involves maximising the investment in the satellite when it is outperforming the core and, conversely, minimising it when it is underperforming. The aim of this dynamic allocation is to produce greater risk-adjusted returns than those produced by static core-satellite management. Like standard CPPI, this dynamic allocation first requires the imposition of a lower limit on underperformance of the benchmark at the terminal date. This so-called floor is usually a fraction of the benchmark portfolio, say 90%. Investment in the satellite then provides access to potential outperformance of the benchmark. Dynamic core-satellite investment has two objectives: to increase the fraction allocated to the satellite when the satellite has outperformed the benchmark and to reduce this fraction when the satellite has underperformed the benchmark. This dual objective can be met with a suitable extension of CPPI to relative risk management. Let P t be the value of the portfolio at date t. The portfolio P t can be broken down into a floor F t and a cushion C t, according to the relation P t = F t + C t. B t is the benchmark. The floor is given by F t = kb t, where k is a constant less than 1. Finally, let the investment in the satellite be E t = ws t = mc t = m(p t - F t ), with m a constant multiplier greater than 1 and w the fraction invested in the satellite. The remainder of the portfolio, P t - E t = (1-w) B t, is invested in the benchmark. In a relative return investment, the core will contain some assets that closely track a given benchmark, whereas the satellite will have assets that ought to outperform this benchmark. This method leads to an increase in the fraction allocated to the satellite when the satellite outperforms the benchmark. An accumulation of past outperformance results in an increase in the cushion and therefore in the potential for a more aggressive strategy in the future. If the satellite has underperformed the benchmark, An EDHEC-Risk Institute Publication 13

14 1. Method: Dynamic Risk Budgeting however, the fraction invested in the satellite decreases in an attempt to ensure that the relative performance objective will be met Extensions 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 F t = kb t. 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 statedependent 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. Capital guarantee floor: this is the most basic expression of a risk budget given by F t = ke -r(t-t) A 0, where r is the risk-free rate (here assumed to be a constant), k a constant <1, and A 0 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: F t = kb t. 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 A t F t = kb t is a minimum funding ratio constraint (Martellini and Milhau 2009). Maximum drawdown floor: extensions of the standard dynamic asset allocation strategy can accommodate various forms of time-varying multipliers and floors. Grossman and Zhou (1996), for example, consider a drawdown constraint that requires the asset value A t at all times to satisfy A t > αm t, where M t is the maximum asset value reached between date 0 and date t: max(a s ) 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 labelled them time invariant portfolio protection strategies (TIPP), and later formalised by Grossman and Zhou (1993) and Cvitanic and Karatzas (1995). This maximum drawdown floor was originally described for absolute risk management, but by taking A t /B t > α max(a s /B s ) s<t, where B t 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 14 An EDHEC-Risk Institute Publication

15 1. Method: Dynamic Risk Budgeting horizon, regardless of the performance of equity markets. More formally, it is given by F t = A t-12, where A t-12 is the portfolio value twelve months earlier. Again, by taking F t = B t-12, for example, this constraint can be extended to relative risk budgets. Conventional strategies consider the floor but ignore investment goals. Goal-directed strategies recognise that an investor might have no additional utility gain once a total wealth G t 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 not immediately clear why any investor would want to impose a strict limit on upside potential. But the intuition is that by forgoing performance beyond a certain threshold, where the relative utility of greater wealth is lower, investors benefit from a decrease in the cost of downside protection. In other words, without a performance cap or goal, investors run a higher risk of missing a nearly attained investment goal. a piece-wise dynamic allocation strategy, with the threshold T t to ensure smoothpasting. The aforementioned floors (capital guarantee, benchmark protection, maximum drawdown constraint, trailing performance) have equivalents in goals. This dynamic risk management approach has a wide variety of applications. Different kinds of floors or the inclusion of goals make possible strategies that meet particular requirements. The inclusion of a maximum drawdown constraint, for example, is of particular interest to open-ended funds, since it lessens the degree to which the investor s performance for the entire holding period depends on the point at which he entered the fund. Thus, asset managers can use maximum drawdown constraints to satisfy the needs of investors who enter and exit at different times. The trailing performance floor is particularly useful for absolute return products, where the investor expects the probability of losing money over any one-year period to be extremely low. We now turn to the discussion of such absolute return strategies. A goal can be accommodated by a strategy in which the fraction invested in the performance-seeking satellite is a multiple m 2 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 m 1 of the distance to the floor. If, in addition, one defines the threshold wealth (denoted by T t ) at which the investor shifts from a goal-oriented focus to a risk-management focus, one obtains An EDHEC-Risk Institute Publication 15

16 1. Method: Dynamic Risk Budgeting 16 An EDHEC-Risk Institute Publication

17 2. Implementing Beyond Diversification: Efficient Absolute Return Indexation Funds of ETFs An EDHEC-Risk Institute Publication 17

18 2. Beyond Diversification: Absolute Return Funds of ETFs Combining equity ETFs and bond ETFs may, as a result of diversification, lead to risk reduction, but weighting stocks and bonds statically does not fully exploit the possibilities of risk management. So this section assesses the ways in which dynamic adjustments of exposure to a bond core (short maturity EuroMTS ETF) and an equity satellite (equity ETF) can ensure that an absolute return fund reaches its objectives. Although there is no single definition of the absolute return concept, most investors interested in such strategies have two main expectations, one having to do with performance management and the other with risk management. In other words, they have a performance target (usually expressed as a multiple of a cash rate or as a constant target) that they expect to hit regardless of market conditions, and they expect to avoid large drawdowns (with a maximum drawdown set at 10% in the application that follows). In an absolute return product, the investor also expects the probability of losing money over any one-year period to be extremely low. Our assumption is that, however equity markets perform, an absolute return product will avoid posting negative returns over a one-year horizon. This constraint can be accommodated with a twelve-month trailing performance floor. We first specify a maximum drawdown equal to 10%, a twelve-month trailing performance floor, and a soft landing objective with respect to a performance cap (investment goal) set at 2.5 times the cash rate. We then proceed with dynamic core-satellite allocation, the core invested in a bond ETF and the satellite in a largecap stock ETF (both in the euro zone); the maximum allocation is set at 50%. Specifically, we combine a core that invests in medium-term bonds (EuroMTS for bonds with three to five years to maturity) and a satellite that invests in an ETF on the EuroStoxx 50 index. The objective is to optimise returns while limiting the drawdown risk of the portfolio to 10%. The intuition behind the maximum-drawdown constraint is that the investment in the risky asset depends not only on risk aversion but also on the margin for error. When the risk budget is spent, one should be prepared to move away from the risky asset. The idea is to benefit from the returns on the stock market ETF if stocks outperform bonds, while securing protection from the downside risk of the equity investment. The data used consists of monthly returns, including reinvestment of coupon or dividend payments, for the period from January 1999 to December The starting period is chosen in this way because the bond data is available starting only with the introduction of the euro, as is usual for euro-denominated bond indices. The strategy for this form of absolute return fund is, of course, one of many possible means of meeting the objectives of absolute return investors. The dynamic core-satellite strategy, in short, is flexible enough to design a broad variety of investment strategies. Exhibit 1 shows the cumulative returns of the strategy we implemented, as well as of the core and the satellite portfolios. In addition, to highlight the built-in protection of this investment strategy, the floor is displayed as well. 18 An EDHEC-Risk Institute Publication

19 2. Beyond Diversification: Absolute Return Funds of ETFs Exhibit 1: Absolute return fund: change in the core, the satellite, the floor and the dynamic core-satellite portfolio We can draw a number of conclusions from this figure. The dynamics of the core portfolio confirm the conservative character of the core investment, but we also see that the performance of the bond core was fairly flat for some extended periods, such as from 1999 to 2000 or from 2004 to The returns of the equity ETF in the satellite portfolio were, by contrast, negative over the entire period. The fluctuations in the value of the large-cap equity ETF in the satellite are tremendous, with a sharp increase before 2000 and steep falls from 2000 to 2002 and in The dynamic core-satellite (DCS) combines the advantages of each of its ingredients, namely the smooth performance of the bond core and the upside potential of the equity satellite. As a result, performance is smooth over the entire period, and cumulative returns at the end of the period are actually higher than those of both the Exhibit 2: Absolute return fund: changes in the allocation to the satellite An EDHEC-Risk Institute Publication 19

20 2. Beyond Diversification: Absolute Return Funds of ETFs core and the satellite. The graph also shows the dynamics of the floor, which reflects the degree of protection. It is likewise instructive to look at the performance in the stock market downturn beginning in the year In fact, the DCS portfolio is largely unaffected. As the portfolio value approaches the floor, the allocation shifts to the core. This behaviour is also illustrated in exhibit 2, which shows the weights held in the satellite portfolio over time. Risk and return statistics for the DCS strategy confirm the conclusions from exhibit 1. In particular, exhibit 3 shows that the average return exceeds that of the core by almost 200 basis points, all while keeping risk as low as that of the defensive core. It should be kept in mind that the conservative nature of the core and the dynamic risk management process are meant to result in smooth returns, in the sense that investors should experience little risk within the entire investment period. Exhibit 3, however, shows statistics that reflect risk at the end of the investment period. Investors obviously care about intrahorizon risk, that is, about losses that occur within the full investment period from December 1998 to December Exhibit 4 shows the returns over rolling periods of one year. We see that the DCS portfolio posts positive returns over most rolling windows of one year. Even for the most recent observations of trailing returns, the strategy generates positive numbers, unlike the satellite, which, for the same periods, posts returns worse than -40%. In fact, the behaviour of the DCS portfolio is similar to that of the defensive core of bonds. Exhibit 3: Absolute return fund: risk and return statistics for the core, the satellite and both static and dynamic core-satellite investments December 1998 December 2008 Average return* Maximum drawdown Volatility* Sharpe ratio*/** Core 4.44% -3.08% 2.61% 0.94 Satellite -0.99% % 19.46% Static core-satellite 2.26% % 9.22% 0.03 DCS 6.41% -3.11% 3.83% 1.15 * annualised statistics; ** risk-free rate fixed at 2% Exhibit 4: Absolute return fund: performance of the core, the satellite and the DCS over a one-year rolling period 20 An EDHEC-Risk Institute Publication

21 2. 3. Implementing Beyond Tactical Efficient Bets: Integrating Predictions Indexation in a Risk-Controlled Framework An EDHEC-Risk Institute Publication 21

22 3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework We have seen that dynamic risk budgeting can ensure sound absolute return management. What is remarkable is the absence of reliance on prediction. Systematic allocation based on past values of the core and satellite portfolios means that the investor bears no forecasting risk. Of course, investment houses may have access to proprietary forecasts that they may wish to use to move the allocation between risk-free and risky assets. In fact, an asset manager may well wish to benefit from his forecasting skill. It is not our objective here to consider how forecasts are best generated. But once they are generated, a crucial question is how to translate them into portfolio decisions. In any forecast-based core-satellite portfolio, of course, the weight of the satellite will increase when the satellite is expected to outperform the core. We consider two ways of translating these forecasts into action. We look first at forecasts used in a standard tactical asset allocation approach that simply increases the allocation to the satellite to a fixed weight when it is expected to outperform and resets it to the lower weight when it is expected to underperform. We then look at whether the manager could actually benefit from using such forecasts of the outperformance of the satellite in the DCS approach Naïve Tactical Allocation Strategy The performance of forecast-based investment depends, of course, on the accuracy of the forecasts. If the forecast is right most of the time, the portfolio should perform well. In this section, we assess the performance of a manager with varying degrees of positive prediction skill. The detailed setup of the analysis is as follows: we simulate an active manager s approach with the following assumptions: If the manager thinks the core will outperform the satellite in the following month he will allocate 100% of the portfolio to the core. If the manager thinks the satellite will outperform the core in the following month he will allocate 50% of his portfolio to the satellite. The remaining 50% is allocated to the core. The manager rebalances his holdings monthly. We assume that the manager has positive forecasting skill, that is, that he correctly forecasts satellite outperformance over a month at least seven times a year. In other words, we assume a hit ratio of at least 7/12. We look at hit ratios ranging from 7/12 to 11/12. To assess the performance of this approach, we simulate 1,000 scenarios for the period from January 1999 to December The investments used in the core and satellite correspond to the previous example, i.e., we use a defensive euro government bond portfolio in the core and a large-cap equity satellite. Each scenario corresponds to a time series of returns for the active manager, given his bets. Thus the 1,000 scenarios represent the returns obtained by 1,000 hypothetical active managers who have a given hit ratio. Exhibit 5 shows risk and return statistics for these scenarios. Since every scenario represents the returns of a hypothetical manager, the average for expected return 22 An EDHEC-Risk Institute Publication

23 3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework Exhibit 5: Forecast-based standard tactical allocation: this table shows results for the tactical asset allocation strategy that shifts allocations based on forecasts of outperformance of the satellite. Results are shown for different hit ratios of forecasts, based on a simulation of 1,000 scenarios. Hit ratio 7/12 8/12 9/12 10/12 11/12 Average expected return Average maximum drawdown Worst maximum drawdown Worst performance over a rolling one-year period 5.68% 8.01% 10.48% 12.91% 15.49% % % -8.49% -6.66% -4.52% % % % % % % % % % % and for maximum drawdown over all scenarios corresponds to the result for the average active manager according to our hypothetical hit ratios. As exhibit 5 predictably shows, higher hit ratios lead to higher average expected returns. But the table also shows that the average for the maximum drawdown statistic computed across the 1,000 hypothetical managers is relatively high even in the presence of positive forecasting skill. For a hit ratio of 7/12, maximum drawdown is, on average, approximately -13%, a figure that reveals the impact of poor forecasts. In fact, even though these managers are right most of the time, they err five months a year, thus exposing the investor to significant downside risk. The average value of risk and return statistics across 1,000 scenarios does not show the impact of manager-selection risk. Using a single manager leads to uncertainty, as results may be much better or much worse than the average across 1,000 managers. First, the results obtained by a single manager depend on the actual hit ratio for the sample period as opposed to his true long-term forecasting ability. Second, given a realised hit ratio, portfolio performance depends on the consequences of his predictions. Predicting outperformance over a month during which the satellite underperforms by 1% is not the same as predicting outperformance over a month during which it underperforms by 10%, even though both are instances of forecast error. Likewise, predicting outperformance over a month during which the satellite outperforms by 10% is more valuable than predicting outperformance over a month during which it outperforms by 1%, though both are instances of forecast accuracy. This dispersion of the managers with the same forecasting ability is shown in the lower panel of exhibit 6. The worst performing manager (or scenario) draws down a maximum of between -28% to -16%, depending on the hit ratio we assume. Likewise, the worst return over a one-year rolling period ranges from -23% to -13%, depending on the hit ratio. So it is clear that relying on active forecasting leads to additional risk, even if the manager is known to have positive forecasting skill. The severe drawdowns shown even for managers with positive forecasting skill underscore the inability of these tactical allocation strategies to provide absolute return portfolios with smooth return profiles. Even with extremely high and An EDHEC-Risk Institute Publication 23

24 3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework clearly unrealistic hit ratios of 11/12 maximum drawdowns are considerably higher than in the absolute-return portfolio based on the DCS approach we described above. So risk control can reduce risk more than forecasting ability can. One naturally wonders if it is possible to combine the return potential of forecasting and downside risk management that would mitigate the high figures for maximum drawdown. As it happens, it may be possible by making the active manager s forecasting ability an integral part of the DCS. We will thus condition the DCS strategy on the return forecasts for the satellite, all while respecting the dynamic risk budget used in the absolute return application above Risk-Controlled Tactical Allocation Strategy Since the main objective is to reduce the drawdown statistics that result from the errors made by skilled forecasters, we impose a maximum drawdown of 10%. Next, we incorporate the manager s forecasting ability by introducing a time-varying multiplier m. If the manager expects the satellite to outperform the core, the multiplier is set to m=5, thus allowing a considerable fraction to be invested in the equity satellite. If the manager expects the satellite to underperform the core, the multiplier is set to m=0. So the portfolio is fully protected from the expected negative performance of the satellite. As before, we simulate 1,000 scenarios to assess the average performance of this risk-controlled strategy. The results in exhibit 6 show the benefits of using DCS management to limit the extreme drawdown induced by forecast error. Again, active management provides high returns that evidently increase as forecasting ability (the hit ratio) improves. However, the approach that makes forecasts part of a DCS approach manages downside risk much better; for a hit ratio of 7/12 the average maximum drawdown is only -7.89%. In the simple tactical allocation strategy, by comparison, the average maximum drawdown is %. This dynamic risk budgeting makes it possible to limit the severe drawdown in the standard tactical allocation. This reduction is more pronounced for relatively low hit ratios. But even with the higher hit ratios it leads to considerable risk reduction. Exhibit 7 shows the reduction in maximum drawdown for each hit ratio. Risk control, then, clearly leads to significant benefits. Exhibit 6: Forecast-based strategy made part of DCS management: the table shows the performance and maximum drawdown of the strategy that integrates forecasts into a DCS framework. Forecasts are based on a simulation of 1,000 scenarios with various hit ratios. Hit ratio 7/12 8/12 9/12 10/12 11/12 Average return 5.96% 7.93% 10.19% 12.57% 15.28% Average maximum -7.89% -7.45% -6.81% -5.89% -4.32% drawdown Worst maximum -9.65% -9.56% -9.51% -9.51% -9.04% drawdown Worst performance over a rolling one-year period -8.57% -8.57% -8.57% -7.96% -7.13% 24 An EDHEC-Risk Institute Publication

25 3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework Exhibit 7: Risk reduction: risk-controlled forecast-based strategy versus standard tactical allocation. This exhibit shows the reduction of the average drawdown obtained by using the DCS approach to implement portfolios based on predictions of outperformance of the satellite portfolio over a one-month horizon. Hit ratio 7/12 8/12 9/12 10/12 11/12 Risk reduction (reduction in 40% 30% 20% 12% 4% magnitude of avg. max. drawdown) The two strategies assume identical forecasting ability. The results demonstrate that the DCS approach reduces the risk of tactical bets based on return forecasts. This application underscores the benefits of DCS investment even for managers who prefer to rely, as it were, on their crystal balls. DCS management may also improve the downside risk management of portfolios when an asset manager wishes to use forecasts to make tactical bets. An EDHEC-Risk Institute Publication 25

26 3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework 26 An EDHEC-Risk Institute Publication

27 Conclusion An EDHEC-Risk Institute Publication 27

28 Conclusion In short, the applications of dynamic risk budgeting described in this paper highlight the potential benefits of using ETFs to gain exposure to several asset classes and the advantages of the dynamic risk management approach. The main benefit is the combination of participation in upside market movements and limited risk exposure. As a result, dynamic core-satellite strategies often offer better risk/return tradeoffs than either core or satellite investments. In addition, maximum drawdown extreme risk is limited. The applications show that, when attempts to add value in constructing portfolios of ETFs are made, risk control may be no less important than diversification and return predictions. The analysis in this paper can be extended in different ways. First, the paper has addressed the shifting weights on allocation to ETFs on stocks and on allocation to bonds. ETF providers have recently issued an increasing number of ETFs on alternative asset classes, such as currencies, commodities, and real estate. It may be worth analysing the integration of such vehicles into a risk-budgeting framework. Second, a simulation study with hypothetical return predictions was used to analyse the strategy with a dynamic multiplier introduced in this paper. A natural extension of this analysis would be to introduce return predictions based on well known stylised facts, such as the relationship between dividend yield and stock returns. These issues are left for future research. 28 An EDHEC-Risk Institute Publication

29 References An EDHEC-Risk Institute Publication 29

30 References Amenc, N., P. Malaise, L. Martellini, and D. Sfeir Tactical style allocation: A new form of market neutral strategy. Journal of Alternative Investments 6 (1): Amenc, N., P. Malaise, and L. Martellini Revisiting core-satellite investing: A dynamic model of relative risk management. Journal of Portfolio Management 31(1): Bakshi, G., and G. Panayotovb First-passage probability, jump models, and intrahorizon risk. Working paper. Black, F., and R. Jones Simplifying portfolio insurance. Journal of Portfolio Management 14: Black, F., and A. Perold Theory of constant proportion portfolio insurance. Journal of Economic Dynamics and Control 16: Brinson, G. P., L. R. Hood, and G. L. Beebower Determinants of portfolio performance. Financial Analysts Journal (July/August). Browne, S Risk-constrained dynamic active portfolio management, Management Science 46 (9): Cvitanic, J., and I. Karatzas On portfolio optimization under drawdown constraints. IMA Volumes in Mathematics and its Applications 65: De Freitas, E., and C. Barker ETFs Tactical asset allocation tools. In Exchange traded funds: Structure, regulation and application of a new fund class, ed. E. Hehn. Berlin: Springer. Deutsche Bank Exchange traded funds Liquidity trends, < dbxtrackers.com>. Estep, T., and M. Kritzman TIPP: Insurance without complexity. Journal of Portfolio Management (summer): Grossman, S. J., and Z. Zhou Optimal investment strategies for controlling drawdowns. Mathematical Finance 3(3): Equilibrium analysis of portfolio insurance. Journal of Finance 51 (4): Hlawitschka, W., and M. Tucker Utility comparison between security selectors, asset allocators and equally weighted portfolios within a selected ETF universe. Journal of Asset Management 9 (1): Kritzman, M., and D. Rich The mismeasurement of risk. Financial Analysts Journal 58(3): Longin, F., and B. Solnik Extreme correlation of international equity markets. Journal of Finance 56 (2): Martellini, L., and V. Milhau How costly is regulatory short-termism for definedbenefit pension funds? Working paper. Miffre, J Country-specific ETFs: An efficient approach to global asset allocation. Journal of Asset Management 8: An EDHEC-Risk Institute Publication

31 About EDHEC-Risk Institute An EDHEC-Risk Institute Publication 31

32 About EDHEC-Risk Institute Founded in 1906, EDHEC is one of the foremost French business schools. Accredited by the three main international academic organisations, EQUIS, AACSB and Association of MBAs, EDHEC has for a number of years been pursuing a strategy for international excellence that led it to set up EDHEC-Risk in With 47 professors, research engineers and research associates, this centre has the largest asset management research team in Europe. The Choice of Asset Allocation and Risk Management EDHEC-Risk structures all of its research work around asset allocation and risk management. This issue corresponds to a genuine expectation from the market. On the one hand, the prevailing stock market situation in recent years has shown the limitations of diversification alone as a risk management technique and the usefulness of approaches based on dynamic portfolio allocation. On the other, the appearance of new asset classes (hedge funds, private equity, real assets), with risk profiles that are very different from those of the traditional investment universe, constitutes a new opportunity and challenge for the implementation of allocation in an asset management or asset-liability management context. This strategic choice is applied to all of the centre's research programmes, whether they involve proposing new methods of strategic allocation, which integrate the alternative class; taking extreme risks into account in portfolio construction; studying the usefulness of derivatives in implementing asset-liability management approaches; or orienting the concept of dynamic core-satellite investment management in the framework of absolute return or targetdate funds. An Applied Research Approach In an attempt to ensure that the research it carries out is truly applicable, EDHEC has implemented a dual validation system for the work of EDHEC-Risk. All research work must be part of a research programme, the relevance and goals of which have been validated from both an academic and a business viewpoint by the centre's advisory board. This board is made up of internationally recognised researchers, the centre's business partners and representatives of major international institutional investors. The management of the research programmes respects a rigorous validation process, which guarantees the scientific quality and the operational usefulness of the programmes. Six research programmes have been conducted by the centre to date: Asset allocation and alternative diversification Style and performance analysis Indices and benchmarking Operational risks and performance Asset allocation and derivative instruments ALM and asset management These programmes receive the support of a large number of financial companies. The results of the research programmes are disseminated through the three EDHEC-Risk locations in London, Nice and Singapore. 40% Strategic Asset Allocation 45.5% Tactical Asset Allocation 11% Stock Picking 3.5% Fees Source EDHEC (2002) and Ibbotson, Kaplan (2000) In addition, EDHEC-Risk has developed a close partnership with a small number of sponsors within the framework of research chairs. These research chairs correspond to a commitment over three years from the partner on research themes that are agreed in common. 32 An EDHEC-Risk Institute Publication

33 About EDHEC-Risk Institute The following research chairs have been endowed to date: Regulation and Institutional Investment, in partnership with AXA Investment Managers (AXA IM) Asset-Liability Management and Institutional Investment Management, in partnership with BNP Paribas Investment Partners Risk and Regulation in the European Fund Management Industry, in partnership with CACEIS Structured Products and Derivative Instruments, sponsored by the French Banking Federation (FBF) Private Asset-Liability Management, in partnership with ORTEC Finance Dynamic Allocation Models and New Forms of Target-Date Funds, in partnership with UFG Advanced Modelling for Alternative Investments, in partnership with Newedge Prime Brokerage Asset-Liability Management Techniques for Sovereign Wealth Fund Management, in partnership with Deutsche Bank Core-Satellite and ETF Investment, in partnership with Amundi The Case for Inflation-Linked Bonds: Issuers and Investors Perspectives, in partnership with Rothschild & Cie The philosophy of the centre is to validate its work by publication in international journals, but also to make it available to the sector through its Position Papers, published studies and conferences. Each year, EDHEC-Risk organises a major international conference for institutional investors and investment management professionals with a view to presenting the results of its research: EDHEC-Risk Institutional Days. EDHEC also provides professionals with access to its website, which is entirely devoted to international asset management research. The website, which has more than 35,000 regular visitors, is aimed at professionals who wish to benefit from EDHEC s analysis and expertise in the area of applied portfolio management research. Its monthly newsletter is distributed to more than 400,000 readers. EDHEC-Risk Institute: Key Figures, Number of permanent staff 47 Number of research associates 17 Number of affiliate professors 5 Overall budget 8,700,000 External financing 5,900,000 Number of conference delegates 1,950 Number of participants at EDHEC-Risk Executive Education seminars 371 Research for Business The centre s activities have also given rise to executive education and research service offshoots. EDHEC-Risk's executive education programmes help investment professionals to upgrade their skills with advanced risk and asset managementtraining across traditional and alternative classes. An EDHEC-Risk Institute Publication 33

34 About EDHEC-Risk Institute The EDHEC-Risk Institute PhD in Finance The EDHEC-Risk Institute PhD in Finance at EDHEC Business School is designed for professionals who aspire to higher intellectual levels and aim to redefine the investment banking and asset management industries. It is offered in two tracks: a residential track for high-potential graduate students, who hold part-time positions at EDHEC Business School, and an executive track for practitioners who keep their fulltime jobs. Drawing its faculty from the world s best universities and enjoying the support of the research centre with the greatest impact on the European financial industry, the EDHEC-Risk Institute PhD in Finance creates an extraordinary platform for professional development and industry innovation. The EDHEC-Risk Institute MSc in Risk and Investment Management The EDHEC-Risk Institute Executive MSc in Risk and Investment Management is designed for professionals in the investment management industry who wish to progress, or maintain leadership in their field, and for other finance practitioners who are contemplating lateral moves. It appeals to senior executives, investment and risk managers or advisors, and analysts. This postgraduate programme is designed to be completed in seventeen months of part-time study and is formatted to be compatible with professional schedules. The programme has two tracks: an executive track for practitioners with significant investment management experience and an apprenticeship track for selected highpotential graduate students who have recently joined the industry. The programme is offered in Asia from Singapore and in Europe from London and Nice. FTSE EDHEC-Risk Efficient Indices FTSE Group, the award winning global index provider, and EDHEC-Risk Institute launched the first set of FTSE EDHEC- Risk Efficient Indices at the beginning of Initially offered for the UK, the Eurobloc, the USA, Developed Asia-Pacific ex-japan, and Japan, the index series aims to capture equity market returns with an improved risk/reward efficiency compared to cap-weighted indices. The weighting of the portfolio of constituents achieves the highest possible return-to-risk efficiency by maximising the Sharpe ratio (the reward of an investment per unit of risk). EDHEC-Risk Alternative Indexes The different hedge fund indexes available on the market are computed from different data, according to diverse fund selection criteria and index construction methods; they unsurprisingly tell very different stories. Challenged by this heterogeneity, investors cannot rely on competing hedge fund indexes to obtain a true and fair view of performance and are at a loss when selecting benchmarks. To address this issue, EDHEC-Risk was the first to launch composite hedge fund strategy indexes as early as The 13 EDHEC-Risk Alternative Indexes are published monthly on com and are freely available to managers and investors. 34 An EDHEC-Risk Institute Publication