Controlling volatility for better investment outcomes

Transcription

1 14 Controlling volatility for better investment outcomes Swings in asset prices are a concern for most investors. Some, however, suffer more than others. Many want the returns that equities can give, but cannot live with the risk created by short-term market swings. One answer is to use a volatility-controlled strategy. Here we outline how a range of such techniques can reduce the risks, while retaining much of the gains to be had from risky assets like equities. We argue that, while the results will inevitably be different, the risk-adjusted returns should be much improved. 1 Investors often use past volatility to predict how returns will turn out in the immediate future. Volatility control techniques use this near-term volatility forecast to adjust exposure to risky assets like equities. How well this forecast works can be seen from Figure 1, where the bars show the normal range of next-day returns (each bar represents one standard deviation either side of the mean) plotted against daily-measured volatility. The lengthening of the bars as they move to the right provides support for the idea that previous volatility may predict future volatility. (The long tails represent the full extent of the range of returns.) Figure 1: Current volatility mapped against future volatility can provide an accurate forecast % Distribution of next-day returns % Measured volatility Daily data 31 December 1927 to 31 December 212. Indices used: S&P 5 Index, adjusted for dividends by Schroders (January 1963 to January 1988), and the S&P 5 Total Return Index (January 1988 to December 212). Results are from a backtest. Certain assumptions have been made. Further details available on request. Source: Bloomberg, Federal Bank of St. Louis and Schroders. Figure 2 illustrates how we might use this observation to try to control the volatility of a portfolio s returns. It shows what happens when we implement a strategy that aims to maintain constant volatility in a portfolio by reducing exposure to risky assets during periods of higher volatility. (We explain how such a strategy might achieve this outcome later on.) The next day s returns are again plotted against recent volatility. The almost uniform height of the resulting bars suggests the strategy is effective at controlling portfolio volatility. It provides evidence that recent volatility can not only help predict volatility, but can also control it and, by extension, the distribution of returns over a longer investment horizon. 1 This article is extracted from a longer paper entitled Managing investment outcomes with volatility control, which is available from the Schroders Portfolio Solutions team on request.

2 Controlling volatility for better investment outcomes 15 Figure 2: A volatility target strategy can dampen experienced volatility Distribution of next-day returns 1 % % Measured volatility For footnotes and source, see Figure 1 on page 14. Having shown how we can control a portfolio s future volatility, it would be natural to ask whether such a strategy might affect the level of returns. To shed some light, we looked at the average returns from a portfolio and compared them with forecast volatility. As Figure 3 shows, we found no clear relationship. It follows that, on average, an investor should not necessarily expect to be rewarded with extra return for extra risk. Looked at another way, we should expect that by reducing risk during periods of higher volatility (say, by reducing exposure to equities), we should be able to improve the expected returns per unit of risk. In technical terms, our portfolio would become more efficient. Figure 3: But actual volatility tells us little about the level of future returns % Average next-day return % Average volatility The population of the daily measures of forecast volatility have been split into 5% bands and the average level of the next day return plotted against the average volatility within each 5%. For other footnotes and source, see Figure 1 on page 14. How is the investor to make use of these findings? We believe there are at least three outcomes that practical investors might want to aim for when managing volatility: The volatility target. This technique seeks to keep the volatility of the portfolio constant. It might be useful, say, for an institution which wants to contain the cost of capital protection provided by options whose price is affected by volatility. If say an equity portfolio is managed to a volatility target of 1%, then tomorrow s exposure should be 1% divided by today s volatility estimate. Thus, if today s volatility estimate is 15%, the allocation to the relevant asset should be 1%/15% i.e. 67%. But if today s volatility estimate is 8%, then the allocation to the asset should be 1%/8% i.e. 125%. The volatility cap. This approach aims to smooth out the more extreme bumps likely to be experienced by an investor. It might be attractive to a saver who wants to be regularly reassured about the value of their savings or an insurance company that needs to control the cost of regulatory capital. It works by monitoring the daily volatility of the portfolio and reducing exposure if it exceeds a predefined level (i.e. the cap). The exposure to the relevant asset is set at the lower of 1% or the volatility cap level divided by today s portfolio volatility estimate. Thus, if we have an equity portfolio with a volatility cap of 15% and today s volatility estimate is below that at 8%, then tomorrow s portfolio will allocate 1% to equities. But if today s portfolio volatility is above the cap at 2%, tomorrow s allocation will be the cap level (15%) divided by 2%, i.e. 75%.

3 16 The variable volatility cap. This aims to limit the risk of the investor losing more than a specific proportion of their portfolio. Any investor who wants to mitigate the risk of extreme capital loss and is happy with soft (i.e. less than absolute) protection might find this useful. Essentially, the implementation formula is the same as for the volatility cap, except that the level of the cap varies according to the level of loss the strategy has incurred from its peak, or high water mark. As losses increase, the volatility cap tightens, reducing exposure and limiting further falls. Thus, for example, if the loss from the highest point is 7%, the volatility cap is set at 1% and the risky asset volatility is 12%, the allocation to the risky asset will be 1%/12% = 83%. As losses increase, the cap level is reduced until it hits a minimum. Thus, say, if the loss rises to 12%, the cap is reduced to 5%, meaning that, if the asset s volatility remains at 12%, the allocation to the relevant asset is 5%/12% = 42%. The characteristics of the three techniques are set out in the table below. It is important to emphasise that, although it is possible to compare each strategy to an appropriate index, the aim here is not to outperform a benchmark but to show how each can improve the certainty of investment outcomes. Volatility target Volatility cap Variable volatility cap Level 15% 15% Varies according to loss level from highest point over previous 12 months Maximum exposure 15% 1% 1% Objective To deliver (broadly) stable volatility over time To deliver volatility no greater than the cap level Aim to limit maximum loss from highest point to, say, 15% on a rolling 12-month basis For illustration only. Source: Schroders. The next table illustrates how the application of these techniques to the S&P 5 over the past 88 years would have affected outcomes for investors. S&P5 Volatility target Volatility cap Variable volatility cap Return (p.a.) 8.3% 8.54% 6.98% 6.57% Volatility 18.76% 14.34% 12.58% 1.18% Drawdown -7.9% % % -2.8% Excess return/risk Daily data 31 December 1927 to 3 November 215. Assumes a one-day lag for implementation of volatility control techniques. For other footnotes and source, see Figure 1 on page 14. Of course, most investors have shorter timescales. Figure 4 looks at outcomes for holding periods of 12 months, comparing each to a pure equities strategy. The first two show that the volatility target and volatility cap strategies have been reasonably effective at keeping volatility at or below the required level, despite wild fluctuations in the underlying asset. The bottom chart confirms that a variable volatility portfolio can help reduce losses at times when the S&P has suffered extreme falls. However, while mitigating extremes, it is not always able to keep losses to 15% over a rolling 12-month period. In all three cases, it is important to note here that, although a volatility controlled investment is driven on a day-to-day basis by the performance of the underlying asset whatever that may be the ultimate returns will differ due to the allocation to that asset varying over time.

4 Controlling volatility for better investment outcomes 17 Figure 4: Our three techniques work in different ways over 12-month periods Volatility for a 15% volatility target % S&P 5 Index Vol. target index Target level Volatility for a 15% volatility cap % S&P 5 Index Vol. cap index Cap level Drawdowns for a variable volatility cap targeting a maximum loss of 15% % S&P 5 Index Variable volatility cap Max 1-year drawdown Daily data 31 December 1927 to 3 November 215. Assumes a one-day lag for implementation of volatility control techniques. For other footnotes and source, see Figure 1 on page 14. Our analysis suggests that volatility control techniques are best used as part of a tailored investment strategy. They may be effective for investors seeking to: stabilise equity volatility and thereby control the spread of outcomes; limit the cost of options used to provide a capital guarantee or to mitigate losses; maximise the benefits for non-investment reasons, such as Solvency II; dampen the volatility of a growth portfolio without investing in bonds. They are less likely to be suitable for investors seeking explicit downside risk protection or if performance is to be measured against a conventional benchmark like an index, as the returns can differ markedly from an indexed portfolio. The variable volatility cap is suitable for those seeking capital preservation, rather than a guarantee. For more certain protection, a put option or guarantee will be needed. Conclusions Volatility management can help investors who need equity-based returns, but with more certainty about reaching their destination. If they set out with the right expectations, we believe volatility management can be a useful part of their investment armoury. Andrew Connell, Head of Portfolio Solutions

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