Index-based building blocks for portfolio management
As participation in the capital markets has increased, and enhanced information flow has intensified the interdependence of various asset classes and geographies, investors have been seeking new sources of uncorrelated returns. Non-traditional asset classes or fund structures—in particular, emerging markets, commodities, real estate, private equity and hedge funds—are now regularly included in the asset allocation mix. Increasingly, investors consider systematic strategies and factor indices across asset classes as additional elements of the investment universe, capable of providing a unique source of diversification and returns. While benchmark indexing has guided investments in equities, bonds and largely in emerging markets and commodities, systematic strategies across asset classes are often approached on a stand-alone and ad hoc basis.
After the credit crisis of 2008, when hedge fund investments experienced steep losses in line with broad asset classes, portfolio allocations came under increased scrutiny. With renewed focus, researchers and market participants sought to analyse active returns and understand the sources of "alpha". The results solidified the understanding that a large part of active returns can be explained by a set of well-established, systematic trading strategies. These strategies can often be understood as risk premia strategies; they capture excess returns by providing insurance against an identified and priced market risk or by providing liquidity in response to a structural market demand. In the same manner that long-only investments in risky assets capture the market risk premium (or "beta"), these strategies capture alternative sources of risk premia available in the markets. To the extent that they are fully non-discretionary and can be described in the form of a set of transparent systematic rules, and thus can be wrapped into an index, they can be thought of as providing access to a form of "alternative beta" rather than "alpha."
The awareness that active investment performance across asset classes can be explained to a very high degree by exposures to common investment styles and markets has led investors to view their investment opportunity set increasingly as risk factors and systematic sources of excess return, rather than as asset classes and individual instruments. Not surprisingly, there has been a continued drive from market participants towards transparency and an understanding of the 'building blocks' of returns, resulting in the ongoing commoditisation of investment strategies.
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This commoditisation of systematic sources of return has a relatively long history in equity investing. Since the publication of the seminal paper on common factors in equity returns by Fama and French in 1993, equity investors have been aware of factor-based approaches that reflect either systematic exposures to themes such as valuation (as measured, for example, by book-to-market ratios), quality (as measured, for example, by the stability of earnings and dividend policies), size (as measured by market capitalisation), momentum (as measured by relative past price performance), or risk anomalies (as measured by the outperformance of low-risk portfolios, compared to high-risk portfolios). See, for example, Melas, D., and Kang, X., 2010, for a discussion of the application of systematic equity indices in the investment process.
While equity factor indices have been discussed at length in previous studies, a more general view of risk premia strategies across asset classes has been lacking. In what follows, we focus on risk premia beyond traditional market betas and equity markets. Strategies that seek to capture these "alternative betas" typically employ techniques unavailable to long-only passive investors. These include non-standard and/or dynamic weightings of index constituents, the use of short positions, leverage, and liquid derivatives. The techniques and instruments used vary widely by asset class.
In the next section, we give a high-level overview of risk premia strategies across asset classes.
SYSTEMATIC RISK PREMIA STRATEGIES ACROSS ASSET CLASSES
Systematic risk premia strategies exist in all asset classes and across a number of investment styles. These strategies have historically been the domain of hedge funds and other active investors; many classic hedge fund strategies are based, directly or indirectly, on risk premia strategies. Well-known examples include currency funds exploiting FX "carry" (long high-yielding currencies, short low-yielding currencies), equity funds involved in stock merger arbitrage (long target stocks, short acquirer stocks), commodity funds exploiting roll congestion strategies (long deferred contracts, short the front contracts), and volatility funds harvesting the short volatility premium (by selling delta-hedged options). Long-only active bond managers may decide to extend the duration of their portfolios (accessing the term premium in fixed income), credit managers may add high-yield overlays (accessing the credit carry risk premium), and equity managers may increase the weight of value stocks in their portfolios (earning the equity value premium). In Figure 2, we categorise systematic risk systematic premia strategies both by asset class and investment theme.
Even complex hedge fund strategies, such as diversified systematic or 'global tactical asset allocation', can also often be broken down into dynamic allocations to a large array of risk premia. Growing awareness of this means that direct (hedge fund) investment is not the only way to acquire exposure to these risk premia. Investable indices that track rules-based strategies can provide investors with transparent access to alternative beta strategies that can offer additional structural sources of excess return.
Two obvious questions arise at this stage:
i) How do we know whether a particular strategy is really a risk premium strategy?
ii) How can we be sure to span all the available risk premia?
Neither of these questions has straightforward answers. To answer the first we employ both qualitative and quantitative analysis. Risk premia strategies exhibit these characteristics:
- They are simple, transparent and based on well-established principles.
- They provide excess returns that can be rationalised by economic intuition.
- The magnitude of the risk premium can vary over time, but since it is compensation for a risk factor, it would require a structural change in the market for it to be "arbitraged away".
- They typically require long and short positions and often the use of derivatives.
- They may involve leverage and dynamic timing.
- They exhibit comparatively stable characteristics, such as volatilities and correlations.
- They have positive excess returns over long periods of time but can be subject to significant draw-downs when the risk (generating the risk premium) materialises. In other words, there may be a negative skew to the returns.
The second question is harder as there is no scientific approach to finding a 'spanning set' of risk premia. We instead rely on a combination of academic research and a survey of investment strategies used by market participants. After reviewing an extensive universe of strategies we have identified eight generic risk premia categories (also see Rennison, Staal, Ghia, and Lazanas, 2011). The generic characterisation is designed to be applicable across different asset classes.
- Carry strategies seek additional compensation by going long high-yielding and short low-yielding assets from a pool of similar assets/instruments. The carry premium is the return for accepting the higher risk associated with the higher-yielding assets.
Classic carry strategy: a carry strategy in FX invests in high yielding currencies, financing them in low-yielding currencies. The FX carry strategy takes on the risk of currency crashes in the investment currencies.
- "Curve" or "term premium" strategies seek additional compensation for taking on the greater uncertainty associated with investing at longer maturities.
Classic curve strategy: the interest rate curve or "term premium" strategy seeks a premium by investing in the longer end of the yield curve and borrowing at the front end, thereby taking on duration risk.
- Value strategies aim to provide excess returns by selecting relatively undervalued assets and shorting relatively overvalued assets in a convergence trade. Given a relevant valuation model, a premium may be available for accepting the long horizon uncertainty associated with the expected value convergence.
Classic value strategy: the equity value strategy is well accepted in the academic world and investment management industry. The basic form is to overweight value stocks (those with high book value-to-market price ratios) and underweight growth (low book-to-market) stocks. This strategy is typically understood to be a long-term strategy that risks missing out on growth-led bull markets. The value strategy is effectively short the "call option" payoff profile of growth stocks, and seeks to earn the associated premium.
- Momentum and trend strategies take positions in instruments based on past price patterns. Momentum strategies go long past winners and short past losers from a pool of assets, taking on the risk of sharp reversals. Trend strategies take long or short directional positions on individual assets, based on recent price movements. In a sense, a momentum strategy is itself a basket of trend strategies. Both strategies can also be understood to rotate across market betas over time.
Classic momentum strategy: the momentum strategy has also been most frequently associated with stock selection, buying recent outperforming stocks and selling recent underperformers. The basic principle can be applied across asset classes.
Classic trend strategy: the trend strategy is most often associated with so called CTA funds. These strategies tend to take positions in portfolios of individually timed instruments across all asset classes. Risk control and portfolio construction is a crucial part of enhanced trend strategy construction.
- Event risk strategies take positions in assets that are affected by specific events which imply some prospect for future price convergence. These strategies attempt to earn any available spread prior to such convergence. They take on various forms of risk, including uncertainty over the timing and completion of convergence, model risk, financing and liquidity risk.
Classic event risk strategy: the merger arbitrage strategy seeks to earn the premium between the target's and the acquirer's stock prices following a merger announcement. The premium is in compensation for the risk of the deal collapsing, among other things.
- Liquidity premium strategies capture the additional compensation available on illiquid instruments.
Classic liquidity strategy: trading off-the-run Treasury bonds versus on-the-runs. Recently issued on-the-run bonds typically have higher demand due to their liquidity and, consequently, may have a lower yield than equivalent off-the-run bonds.
- Volatility premium strategies seek to earn the premium available between implied volatility and realised volatility in options markets, by taking on the risk of spikes in realised volatility.
Classic volatility strategy: systematically selling delta-hedged straddle options is a standard approach to capture the volatility premium that applies across asset classes. Where available, the short selling of variance swaps provides a clean method of targeting this premium.
- Emerging Markets premia may be available for those investing in certain emerging market instruments, reflecting the increased political and economic uncertainty associated with these markets.
Classic Emerging Markets Strategy: money market rates are typically higher in emerging market currencies, so a strategy which goes long emerging market deposits may earn a premium over developed market deposit rates, albeit with potentially higher currency risk.
However, not all risk premia can be captured with transparent, liquid and cost-effective systematic investment strategies. For this to be possible the underlying constituents must be liquid, have reasonable trading costs and prices that are publicly available. In Figure 3 we list simple implementations of several risk premia strategies that have the above characteristics and report their historical performance. The results illustrate that the Sharpe ratio of these simple implementations is in line or higher than the Sharpe ratios of classic market risk premia. However, the volatility of systematic risk premia strategies is generally lower than the corresponding market risk premia. Because these strategies were created without any in-sample optimisation of their parameters, it is reasonable to expect that their characteristics will persist out-of-sample.
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Although not shown in Figure 3, the correlations between risk premia strategies, as well as those between them and their respective market betas, are low. However, certain distinctive patterns emerge which suggest that risk premia strategies can be divided into three groups:
- Risk-on premia tend to exhibit positive correlations with their respective markets, increasing during market crises. Carry premia are typically in this category.
- Neutral risk premia are generally market-neutral. Event risk premia can be in this category if the occurrence of the risky event is not dependent on the market's behaviour.
- Diversifying risk premia tend to exhibit positive returns during bear markets. Value and trend/momentum strategies fall into this category.
Together with market beta exposure, risk premia strategies provide the building blocks for a systematic investment process. They can be used to increase diversification, add additional sources of return to existing portfolios, enrich tactical asset allocation, and even hedge long-only balanced portfolios.
So far we have introduced a systematic view of common investment themes and approaches across asset classes. A detailed discussion of each of these strategies is beyond the scope of this article. However, to illustrate some of the practical applications of these strategies beyond the relatively well-known case of equities, we present a number of case studies across asset classes.
CASE STUDY: FACTOR INDICES IN COMMODITIES INVESTING
Investors have increased their allocations to commodities dramatically over the past decade. While most investors approach commodity investing through index-based investments, recent years have seen a broader awareness of alternative commodity strategies that aim to provide additional sources of return.
Commodity markets provide a rich source of investment opportunities, many of which are structural in nature. These structural drivers of commodity returns and risk are often less well understood by investors than the returns and risks deriving from other asset classes. Commodity investing reflects a broader trend in the financial markets towards the commoditisation of alpha, particularly since the early 1990s, when the first commodity indices were launched. Increasing demand for active management led to the development of a wide range of investable indices that provide access to dynamic commodity strategies.
The primary drivers of the returns of commodity strategies are not always clear. Whereas most financial assets can be understood and valued through a cash flow discounting approach, commodities are different. They are not capital assets and are subject to unique supply and demand forces that lead to complex valuation dynamics. The structurally different needs and incentives of producers, consumers and speculative market participants (both active and passive) create investment opportunities that go beyond the passive long-only exposure favoured by the majority of investors.
While investment themes might apply conceptually across asset classes, the implementation of the portfolio management decision is specific to each asset class at hand. With only a small number of investors trading commodities outright, the majority of market participants gain exposure to this asset class through the futures market. In particular, most systematic commodity strategies are composed of futures contracts on multiple commodities (often with different maturities), rather than physical commodities. Since any futures-based commodity strategy comes down to a choice of individual commodities, futures contracts of varying maturities, and decisions on trading times and investment periods, an understanding of the term structures of commodity futures contracts can provide valuable insight into the nature of commodity strategy returns.
Curve in commodities. A simple implementation of the curve premium strategy invests systematically in a deferred (forward) futures contract (subject to tradeability constraints, since liquidity decreases rapidly as one moves further along the futures curve). We select the three month deferred point as it provides a good trade-off between the available roll return and liquidity.
Value in commodities. Value strategies can take many forms, but in commodities they often aim to capture the relative tightness of supply versus demand. Value strategies can be designed to take advantage of the negative relationship between inventories and prices by targeting commodities with the highest relative upside potential, which means commodities with low levels of inventories. The degree of backwardation of the futures curves (i.e. the extent to which futures curves are downward sloping) provides a price-based proxy for the tightness of supply of individual commodities. A simple value strategy will allocate to a portfolio of the most backwardated commodities while shorting a benchmark portfolio.
Trend in commodities. Trend strategies provide a mechanical approach to capturing persistent pricing phenomena in the markets. As such, they are likely to capture different behavioural and risk premia effects over time. Non-profit-seeking objectives by certain market participants can also lead to trending prices. Typical examples in commodities include producers implementing their hedging programs and central banks increasing their gold reserves. Trending in commodities can also be related to persistent changes in the level of inventories. A very simple implementation of a commodities trend strategy uses the nearby futures contract price change over a historical rolling window as a signal to determine whether to take a long, short or neutral position in the particular contract.
Liquidity in commodities. Liquidity strategies anticipate the investment needs of passive investors and are positioned to exploit potential liquidity shortages. Typical liquidity strategies capture the risk premium generated by taking a long position in commodities during a rolling period outside the standard windows utilised by the main benchmark indices. Liquidity provision to other market participants by speculative investors is a core feature of the commodity market. While specific roll schedules and benchmark flows might seem like a transient source of excess returns, there are no indications that the supply of liquidity will increase to eliminate the excess returns provided by such strategies.
Figure 4 shows a table summarising the performance of commodity risk premia strategies, by comparison with the benchmark commodity index (S&P GSCI Light Energy). We show here the annual excess return, annual risk (as measured by the realised volatility) and Sharpe ratios. Liquidity and curve strategies have delivered the highest historical Sharpe ratios, 2.38 and 2.01 respectively. Trend has performed less well, but it should be remembered that it is not market-neutral at any point in time and, as such, significantly outperforms the benchmark long-only investment.
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Figure 5 shows the correlation matrix for the four risk premia strategies, including the S&P GSCI Light Energy index as a market benchmark. Correlations below the diagonal are calculated using monthly returns from January 2000 to October 2011. Correlations above the diagonal are calculated during the period around the credit crisis from July 2007 to March 2009, when we observed a considerable increase of volatility in commodity markets. The four commodities risk premia strategies have low or negative correlations with the market benchmark. Rather than increasing during the crisis, the correlations become negative indicating diversification potential within the asset class.
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CASE STUDY: PORTFOLIOS OF RISK PREMIA AND ASSET ALLOCATION
As the understanding of the properties of risk premia strategies has deepened, an increasingly popular concept is the formation of portfolios of risk premia strategies that are broadly diversified across investment styles and asset classes. Such portfolios can then represent a valuable additional constituent in the asset allocation exercise.
As discussed above, the returns of risk premia strategies fluctuate but are generally positive over time: their volatility is relatively low and the correlations between them are low and relatively stable. In addition, our research shows that certain styles of risk premia have complementary return profiles. For example, momentum strategies combine well with carry and value strategies across asset classes. Because of these properties of the underlying risk premia strategies, broadly diversified portfolios of risk premia tend to have more stable performance then individual strategies. As a result, such portfolios can have attractive risk/return profiles with relatively low potential drawdowns, as well as low and stable correlations to traditional asset classes.
However, in order to benefit from these attractive properties, attention must be paid to portfolio construction. While the old adage claims that diversification is the only free lunch in financial markets, a careless cook can ruin that lunch. Strategy combination approaches range from simple weighting schemes to sophisticated quantitative and view-based approaches that aim to provide additional value through the optimal incorporation of non-price-based information. As always, the golden rule is to achieve the proper balance between robustness and sophistication.
Increasingly, investors in systematic strategies approach portfolio construction on the basis of risk allocation, employing a range of methodologies that achieve better diversification than simple allocation techniques while at the same time avoiding the potential lack of robustness of full statistical optimisation procedures, such as mean-variance analysis. For example, a portfolio construction method involving the equal weighting of notional exposures ignores relative risk contributions (the commodity value strategy of the previous section is almost three times as volatile as the curve strategy, so should they be weighted equally?) and fails to diversify the portfolio properly. By contrast, equal risk-weighting of strategies ensures that predicted portfolio risk can be equally attributed to each of the strategies in the portfolio (equal-risk-weighting is optimal in a mean-variance framework under the assumption that ex-ante Sharpe ratios across constituents are assumed to be the same, and as such can be considered to be a good starting point for overlaying views). Tactical allocation and optimal incorporation of views is a rich source of investment performance for active investors.
As an example of a portfolio diversified across styles and asset classes, we create a basket of tradeable Barclays Capital systematic strategy indices that give exposure to various risk premia across equities, rates, commodities, currencies, and volatility. We employ equal volatility weighting to construct the portfolio as a simple implementation of a risk-weighting approach. Figure 6 illustrates the resulting index over the past ten years. Note that the example shows back-tested performance1. Historically, the portfolio has achieved stable returns with relatively low levels of risk and relatively minor drawdowns.
Figure 7 shows average returns for the risk premia portfolio, compared with average returns on the S&P 500. The returns are grouped by the sorted deciles of the benchmark index. In other words, from the left to the right we show firstly the average for the 10% lowest returns on the benchmark over the past decade, and lastly the average for the 10% highest returns, which are compared with the average strategy portfolio return over the same time periods. The strategy basket performs well, independent of the return of the benchmark. Remarkably, some of the highest average returns are realised when the equity market suffers most.
Figures 6 and 7 illustrate the attractive properties of portfolios of risk premia strategies as components in asset allocation.
CASE STUDY: TACTICAL ALLOCATION TO THE FX CARRY RISK PREMIUM
The FX carry trade is well-known and is a good example of a risk premia strategy. It has been pursued as a source of performance by hedge funds and other active investors for many years. The carry trade is profitable over the long run but can encounter significant losses over short periods.
Changes in interest rate expectations and global risk appetite can lead to the abrupt unwinding of carry trades. During these episodes, the typical carry pattern reverses and investment currencies sharply depreciate while borrowing currencies appreciate. We construct an FX implied volatility index based on (three-month) at-the-money-forward volatilities to measure the state of yield-seeking behaviour and risk aversion among investors. Figure 8 highlights the potential of using this risk indicator as a signal to time the currency carry premium. The strength of the relationship between returns and market-wide risk aversion is striking.
We implement a simple example of tactically allocating to a risk premia strategy based on this observation. When the volatility-based signal indicates a risk-seeking environment, we are fully invested in the carry trade (long carry), while a risk aversion signal requires us to enter into a short carry position. The trigger level for risk aversion was calibrated to reflect an expected frequency of the risk aversion environment that corresponds to the long-run historical frequency of substantial negative performance of the carry trade. Figure 9 shows that the FX carry strategy could (at least on an ex-post modelled basis) be timed successfully by taking into account investor risk aversion. The availability of risk premia indices as building blocks for portfolios allows investors to focus on allocation and risk management decisions.
A deeper understanding of systematic sources of risks and return beyond equities and broad market exposures could have significant implications for institutional asset management. Risk premia strategies across asset classes have the potential to help investors achieve better diversification and offer additional sources of performance. They can play an important role in strategic and tactical asset allocation and provide a starting point for more complex strategies that tailor the underlying source of return to the desired investment objectives. In short, they provide the building blocks for the next generation of active portfolio management.
While many systematic investment strategies are already available as investable indices, research into risk premia strategies across asset classes is likely to continue to drive the development of cost-effective, transparent and investable indices. This focus on the building blocks of asset management will be accompanied by renewed interest in tools and insights to utilise these return drivers in an optimal way. Systematic strategies and indices tracking risk premia will become more numerous, sophisticated and diverse.
Endnotes And References
- Live performance of this portfolio is available on Bloomberg under the ticker BXIIXARP (the live version is scaled to achieve 3% volatility on average).
- The FX carry trade consists of borrowing in low interest rate currencies ('funding currencies') and investing in high interest rate currencies ('investment currencies'). The returns on this trade are determined by the carry (i.e. the interest rate differential between currencies) and movements in exchange rates of the traded currencies.
- Barreto, M., Corsi, M., Ghia, K., Lazanas, A., Norrish, K., Molina, R., and Staal, A., 2011. Commodity Markets Investment Insight: Structural Sources of Excess Return, Barclays Capital Research.
- Chatterjee, A., Ghia, K., Rennison, G. and Staal, A., 2010. The G10 FX Carry Premium: Timing and Refining the Currency Crash Risk Premium, Barclays Capital Research.
- Chow, T., Hsu, J., Kalesnik V., and Little, B., 2011. A Survey of Alternative Equity Index Strategies, SSRN Working Paper Id 16963331.
- Fama, E., and French, K., 1993. Common risk factors in the returns of stocks and bonds, Journal of Financial Economics, 33 (1).
- Melas, D., and Kang, X., 2010. Applications of Systematic Indexes in the Investment Process, Journal of Indexes (September/October).
- Rennison, G., Staal, A., Ghia, K. and Lazanas, A. 2011. Barclays Capital Risk Premia Family: Sequencing the strategy genome, Barclays Capital Research.
- Rennison, G. and Staal, A., 2010. Does anything beat equal volatility weighting?, Barclays Capital Research publications.