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Q2 2016

Hermes Investment Office Market Risk Insights

Each social formation, through each of its material activities, exerts its influence upon the civic whole; and each of its ideas and ideals wins also its place and power. Patrick Geddes – sociologist and urban planner In our second Market Risk Insights of 2016, we again try to add some colour to the current environment. The markets were skittish in the first quarter, as we predicted. Even the sociologist quoted above, as familiar as he was with human interaction, would have been shocked by the speed with which the mood of the market changed, seemingly much faster than in the past.

Oil alone fell nearly 30%, rallied some 27%, dropped over 22%, recovered This makes it even more challenging for and fund managers to almost 60%, before falling a final 8%; an exhausting time for market navigate the markets. We cannot underestimate the risks of real-world observers. Oil spiked 81% by mid-February, while collapsing scenarios. But as investors we must bear some risk in order to generate back below its starting level of 48% towards the end of the quarter. returns, and must broaden and deepen our understanding of risk But it wasn’t just oil, all risky assets were chucked about. beyond traditional measures to capture the full picture.

Risk is best considered a multi-headed hydra, one that changes shape Figure 1: Oil: price v volatility dramatically through time depending on market conditions. As such, 50 100 understanding the impact of market risks requires close analysis of the risks at hand. 45 90

40 80 Oil volatility index Summary 35 70 Key risks highlighted in this report:

Oil price 30 60 „„Volatility will spike again this quarter „„Correlation risk has not disappeared 25 50 „„Liquidity risk could easily progress from being a concern 20 40 to a problem 14 Jan 16 14 11 Feb 16 11 Feb 28 Jan 16 28 25 Feb 16 25 Feb 31 Dec 15 31 31 Mar 16 31 10 Mar 16 Oil price (LHS) Oil volatility (RHS) We group our thinking into five key aspects of market risk:

Source: Hermes, Bloomberg, CBOE as at 31 March 2016. 1. Volatility 2. Correlation risk Recent work by UBS supports the notion that sentiment was much 3. Stretch risk more persistent in the pre-financial crisis era than it is today, with the regularity of nervous peaks shrinking from nine-to-12 months 4. Liquidity risk to as little as three months today. 5. Event risk While investors must also consider the full gamut of risks, beyond pure financial-market risks, in this paper we will leave our analysis of the wider context for another day.

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Volatility Figure 3: The volatility of volatility Looking solely at volatility has its pitfalls, but it remains a core building block for all risk analysis. The key is to consider forward-looking volatility through 16 several different lenses and across multiple asset classes. 14

Figure 2 shows the 52-week of the VIX, the Merrill 12 Option Volatility Expectations (MOVE) Index, the Deutsche Bank 10 FX Volatility (Currency VIX) Index and the expected volatility of the Bloomberg Commodity Index (Commodity VIX). These measure 8 the implied volatility of equity markets, markets, 6 currency markets and commodity markets respectively, and have been VVIX index values standardised to make them directly comparable. They each represent 4 the market’s expectation of future volatility, and are often viewed as a 2 benchmark of risk appetite. 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Figure 2: Moving averages of selected volatility measures VVIX reading 1-year moving average

4 Source: Hermes, Bloomberg, CBOE as at 31 March 2016.

3 Forward-looking volatility predictions rose in two distinct spikes during the quarter, the first during January and the second in late February 2 and early March, before returning to the longer-term moving average 1 level each time. Forward-looking volatility has not reached the heights of that first spike since 2011, aside from a peak last August. We 0 expect further spikes at a higher frequency as 2016 unfolds, a reflection Normalised index of growing uncertainty. -1 We also look at cross-sectional dispersion as a volatility measure. It can -2 be thought of as a measure of the various opportunities available for 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 pickers in equity markets, reflecting the best-to-worst range at VIX MOVE Currency VIX Commodity VIX particular points in time.

Source: Hermes, Bloomberg, CBOE, Deutsche Bank, Bank of America Merrill Lynch as at Figure 4: Cross-sectional dispersion of stock returns 31 March 2016. 20 Equity volatility rose dramatically as the first quarter unfolded, peaking 18 around the middle of February, before falling back to lower levels. However, 16 our normalised measure remains substantially above the average for 2015. 14 Bond volatility barely moved over the quarter, dipping modestly in the 12 second half, and remains well below its 2015 average. Currency volatility rose steadily as sentiment on the US dollar changed rapidly, while volatility 10 in the commodity and currency markets remained elevated at levels last 8

Correlation signal seen in 2013. A number of factors have led to an increase in market speed, 6 crowding, herding and short-term liquidity evaporation, and we would 4 anticipate that these will continue to lead to sudden drops and spikes in the 2 markets during the second quarter. 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Expectations for future volatility can be seen in the VVIX, a risk-neutral forecast of large-cap US equity index volatility. Cross-sectional volatility 12-month moving average

Source: Hermes, Bloomberg, FTSE as at 31 March 2016.

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For European equities, dispersion rose above and then fell back to its longer- term moving average during the quarter, a move that was echoed in almost Correlation risk all regional equity markets. With a dampened risk environment taking hold Looking at volatility in isolation runs the risk of being both meaningless as the quarter progressed, dispersion decreased in most markets, even and misleading – we must also consider correlation, which measures though the -term trend of increasing dispersion remains intact. Cross- the relationship between assets in a portfolio. Correlation is the second asset dispersion also continued its upward trend, suggesting an improving building block upon which the notion of diversification is grounded and, asset-picking environment all round. much like volatility, it is highly time variant.

A new indicator for this quarter is the variance risk premium (VRP). It As investors, we must be careful about our use of the term correlation. measures the difference between market-implied volatility and realised Two variables with the same long-term trend could have a negative, risk. It is essentially a contrarian indicator, in that when it is high and short-term correlation coefficient, over-emphasising the level of positive it suggests that market participants are overly pessimistic about diversification available between them. Information regarding the market risk, and vice versa. long-term trend should be taken into consideration when assessing diversification. Given that correlation is typically measured with respect to mean values, we should also account for sample error. Figure 5: US equity variance risk premium

300 The markets appeared marginally more correlated at the end than at the beginning of the first quarter. However, the journey between those 200 two points was as traumatic as the trend in volatility. Across the quarter 100 we saw an increased correlation of all assets with oil.

0 Figure 6: Correlation of oil with US equities -100 Risk difference 0.5 -200 0.4 -300 0.3 -400 0.2 0.1 Correlation 1 Feb 08 9 Oct 15 11 Jun 10 9 May 13 17 Apr 15 27 Jun 08 29 Apr 14 21 Oct 14 31 Oct 13 21 Nov 11 23 Jun 09 31 Mar 16 31 May 11 17 May 12 12 Nov 15 Dec 09 03 Dec 10 22 Dec 08 0 Variance risk premium Z-score -0.1 -0.2 Source: Hermes, Deutsche Bank as at 31 March 2016. -0.3 When pessimism is highest, it could be an opportunity to buy risky 7 Jan 16 4 Feb 16 3 Mar 16 21 Jan 16 14 Jan 16 11 Feb 16 11 Feb 16 28 Jan 16 18 Feb 16 25 Feb 16 31 Dec 15 17 Mar 16 31 Mar 16 10 Mar 16 assets. During the quarter VRP spiked and then rapidly dropped, 24 Mar 16 highlighting the swings in market sentiment. Correlation (S&P500 Index,WTI Oil) We anticipated that 2016 could be a bumpy transition, but the start Source: Hermes, Bloomberg as at 31 March 2016. of the year was even rougher than we predicted. We expect the macro environment to remain fluid and volatile, so investors must stay nimble and able to take advantage of opportunities as they arise. With equity Early signs for the current quarter indicate a decline in the Oil/S&P 500 volatility falling to about 30% below its long-term average, things may correlation and a weakening of the US dollar/S&P 500 correlation too. appear too calm to be true. This will help to remove some market tail risk, but there is still a chance that the higher drift in correlations could resume.

Analysing correlation surprise allows us to capture the degree of statistical unusualness in current correlation levels relative to history. Figure 7 shows a correlation surprise index that the Hermes Investment Office has created to demonstrate this effect. In general we can see that spikes in correlation surprise are more often than not followed by disappointing returns.

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Figure 7: Correlation surprise in the global equity universe Figure 9: Correlation signal

1 400,000 0.8 350,000

300,000 0.6

250,000 0.4

200,000 Correlation 0.2 150,000 0

Correlation surprise index 100,000 -0.2 50,000

0 -0.4 1999 2001 2003 2005 2007 2009 2011 2013 2015 2016 2004 2006 2008 2010 2012 2014 2016

Source: Hermes, Bloomberg as at 31 March 2016. 80

70

Figure 8: Correlation surprise and returns, 31 December 1998-17 April 2015 60

0.00 50 40 -0.05 30

-0.10 Correlation signal 20 -0.15 10

-0.20 0

-0.25 -10 2004 2006 2008 2010 2012 2014 2016 Subsequent one-month annualised return -0.30 Russell MSCI MSCI MSCI MSCI 3000 Emerging Emerging Europe China Source: Hermes, Bloomberg as at 31 March 2016. Markets Asia

Average return after correlation surprise To overcome these issues, we can think of a correlation signal metric as the average correlation divided by the of the Source: Hermes, Bloomberg as at 31 March 2016. coefficient. This can serve as a guide to the stability of the correlation and whether it is viable as a basis for inference. For example, the This index remained subdued for much of the quarter, rising to a correlation between global equities and high- bonds remained high moderate peak mid-March, far below the levels seen in 2014 and 2015. over the quarter, with a brief burst of instability at the beginning of The spike captured global markets acting in unison as they recovered March as credit markets threatened to dislocate from equity markets. from the mid-February lows. The longer-term moving average declined steadily over the quarter. Until relatively recently, asset managers could always rely upon some basic tenets: markets generally respond to changes in their own A key challenge in investment management is the variation in fundamentals, diversification can be achieved across asset classes and correlation levels as assets or asset classes that appear to be different investment strategies generate independent returns. But uncorrelated often become highly correlated during periods of market in an environment in which assets move in tandem, those tenets do stress. Conversely, those that are highly correlated may de-couple at a not necessarily hold. This phenomenon is commonly referred to as later time. This instability in the level of correlation is further aggravated risk-on risk-off (RoRo) and broadly sees assets split into those that are by time-dependency in the volatility of the correlation coefficient. At perceived as risky and those that provide a safe haven. times, correlations appear to fluctuate within a tight range, at others, we see fluctuations in the sign of correlation in very short time periods. We can create an index, based on decomposing correlations using principal component analysis (PCA), which captures the strength of the RoRo phenomenon at any given point in time. During risk-on periods, risky assets in unison and safe-haven investments fall. We witness the opposite during risk-off periods, capturing the mood of the market as it oscillates between optimism and pessimism.

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Figure 10: Risk-on/risk-off signal Stretch risk Stretch risk allows us to identify assets that trend in one direction for a 0.6 considerable period of time, suppressing headline volatility and giving the impression that it is less risky than is actually the case. 0.5

0.4 Figure 12: Stretch risk – credit

0.3 800

0.2 700 600 0.1 500 0 400

300 23 Mar 13 23 Mar 16 23 Mar 01 23 Mar 10 23 Mar 07 23 Sept 11 23 Mar 92 23 Mar 95 23 Mar 98 23 Mar 04 23 Sept 14 23 Sept 93 23 Sept 99 23 Sept 02 23 Sept 96 23 Sept 05 23 Sept 90 23 Sept 08 200 RoRo 3-year moving average Long-term average Regime average 100

Source: Hermes, Bloomberg, HSBC as at 31 March 2016. 0 Jul 11 Jul 12 Jul 15 Jul 13 Jul 14 Jul 10 Jan 11 Jan 12 Jan 15 Jan 13 Jan 16 Jan 14 Apr 11 Jan 10 Apr 12 Apr 15 Apr 13 Oct 11 Apr 14 Apr 10 Oct 12 Oct 15 Oct 13 Oct 14 Oct 10 The RoRo index ground steadily higher over the first quarter, above Mar 16 its longer-term average, suggesting we should be cautious about S&P/ISDA CDS US High Yield Index diversification assumptions. Source: Hermes, Bloomberg, S&P as at 31 March 2016. Figure 11: Correlation heat maps In the past, we have illustrated this with reference to the credit markets, Correlations: March 2016 and commented on the turning point reached in late 2014. The market Global HY US NonFin HY Constrained MSCI EM for credit-default swaps on US high-yield bonds, shown in figure 12, EU N-FinaFixed&Float HYC MSCI EUROPE MSCI NORTH AMERICA moved steadily higher thereafter until mid-February 2016, when we BBG Industrial Metals Australia Govt Bonds Generic BBG Industrial Metals saw a sharp reversal, the largest decline of credit spreads on record. BBG Energy BBG Agriculture BBG Livestock Despite this recent move, we suspect that stretch risk remains firmly MSCI JAPAN Australia Govt Bonds Generic Euro Generic Govt Bond 10Y on the wane in the world of corporate bonds and that the likelihood of Germany Generic Govt 10Y BALTIC DRY INDEX underestimating actual risk levels is receding over time. Japan 10 YEAR JGB FLOAT BBG Precious Metals Global Broad Market Figure 13: Stretch risk – commodity momentum MSCI.EM Global.HY BBG.Energy MSCI.JAPAN BBG.Livestock MSCI.EUROPE BBG.Agriculture 250 BALTIC.DRY.INDEX BBG.Precious.Metals Global.Broad.Market BBG.Industrial.Metals MSCI.NORTH.AMERICA Eu.N.FinaFixedFloat.HYC US.NonFin.HY.Constrained 200 Australia.Govt.Bonds.Generic.Y Germany.Generic.Govt.10Y.Yield Japan.10.YEAR.JGB.FLOATING.RA Euro.Generic..Govt.Bond.10Y.Yield

150 Correlations: December 2015 US NonFin HY Constrained EU N-FinaFixed&Float HYC MSCI EM 100 Global HY

MSCI EUROPE BCOM Index MSCI NORTH AMERICA MSCI JAPAN Australia Govt Bonds Generic 50 BBG Industrial Metals BBG Energy BBG Agriculture BBG Livestock Japan 10 YEAR JGB FLOAT 0 BALTIC DRY INDEX Germany Generic Govt 10Y Euro Generic Govt Bond 10Y BBG Precious Metals Jan 11 Jan 15 Jan 13 Jan 91 Jan 01 Jan 97 Jan 07 Jan 95 Jan 93 Jan 99 Jan 05 Jan 03 Global Broad Market Jan 09

Bloomberg Commodity Index MSCI.EM Global.HY BBG.Energy MSCI.JAPAN BBG.Livestock MSCI.EUROPE BBG.Agriculture

BALTIC.DRY.INDEX Source: Hermes, Bloomberg as at 31 March 2016. BBG.Precious.Metals Global.Broad.Market BBG.Industrial.Metals MSCI.NORTH.AMERICA Eu.N.FinaFixedFloat.HYC 0.YEAR.JGB.FLOATING.RA US.NonFin.HY.Constrained .Generic.Govt.Bond.10.Year Australia.Govt.Bonds.Generic.Y Germany.Generic.Govt.10Y.Yield Commodity market stretch risk remains firmly in place, as commodities continued to head towards lows last seen in the late 1990s, and the risk Source: Hermes, Bloomberg as at 31 March 2016. of a reversion has stayed high as a result. The steady slide in commodity prices may be understating the true risks of this asset class. The correlation heat map reveals a modest uptick in the measure of correlation stability, which points to likely changes in correlation. For Valuations can also become very stretched without the appearance that reason, we suspect that markets as a whole are somewhat more of increased volatility. Assets or markets become extremely cheap or fragile than correlation risk would suggest. Our correlation surprise expensive through continual small price movements. However, such metric remained largely subdued over the quarter. valuations rarely persist and a snap back or reversal in value is likely to occur, with the relevant asset or market returning to fair(er) value.

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Figure 14: Stretch risk – valuations Figure 15: Funding and credit risk

1.4 8 7 1.2 6 1 5 0.8 4

% 3 0.6 2 0.4 1 0.2 0 -1 0 -2 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Apr 11 Apr 12 Apr 15 Apr 13 Apr 14 Apr 10 Apr 01 Apr 07 Apr 02 Apr 05 Apr 03 Apr 09 Apr 06 Apr 04 Apr 08 YTD 16 Ratio of MSCI Emerging Markets Index price-to-book value to MSCI World Index TED spread Credit spread price-to-book value

Source: Hermes, Bloomberg, MSCI as at 31 March 2016. Source: Hermes, Bloomberg as at 31 March 2016.

In figure 14 we compare the price-to-book value in developed equity Both measures remain subdued at pre-crash levels. This indicates that markets to that in emerging markets. The ratio has modestly reverted over liquidity remains at a reasonable level in the money markets, and that the first quarter, reflecting a reduced difference in valuations between liquidity concerns have not fully engulfed the credit markets yet. The developed and emerging markets relative to their long-term history, as a two measures moved in opposite directions during the quarter, but not result of the universal volatility. sufficiently to assume liquidity.

Both equities and bond markets experienced sharp declines followed by Low trading activity appears to be a feature across almost all markets steep recoveries over the quarter. Momentum-based stretch risk also and is clearly contributing to an increase in the number of violent declined during this period, as markets responded to macroeconomic dislocations in asset prices. Taking the foreign-exchange markets as an developments. Equally, equity markets have taken their first steps example, declining interbank in all but a few major currency towards restoring longer-term homogeneous relationships. We remain pairs continues to result in liquidity shocks becoming more frequent and wary of . liqudity risks should stay prominent in investors’ minds during 2016.

Dislocations can occur in markets for even highly liquid assets as Liquidity risk liquidity can come and go. Moreover, the liquidity of an asset often Investigating the relationship between market risk, funding and depends on the direction in which you wish to trade and the direction monetary liquidity is essential in today’s markets. Funding refers to that the rest of the market wishes to take. Add to that the volume of the ease of borrowing, whereas monetary liquidity reflects the ease of the trade, and we introduce another variable that influences liquidity. monetary conditions. They influence market liquidity, through market- By identifying ‘crowded’ trades, we are able to identify potential triggers making activity and bank funding respectively. of liquidity risk. The monthly survey conducted by Bank of America The two most closely followed metrics for funding and liquidity risk Merrill Lynch of global fund managers provides some clues. are the TED spread and the Credit spread. The former focuses on the difference between the interest rates available in the interbank market Figure 16: Fund managers answer the question: where do you think the most and those on short-term US government debt, typically at a one- or crowded trades currently are? three-month view. The latter generally focuses on the spread between corporate bonds and government bonds, again at a short maturity. The Other credit spread is thus an indicator of perceived credit risk, linked closely Long quality to the potential for default in the corporate bond market. Short treasuries Short high-yield credit Long US FANG Short emerging markets Short oil Long Eurostoxx 50 Long EU periphery debt Long US dollar

0% 5% 10% 15% 20% 25% 30% 35%

Apr 16 Mar 16 Feb 16

Source: Hermes, Bank of America Merrill Lynch as at 13 April 2016.

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A number of popular trades fell away rapidly during the quarter, such as long US dollar and short oil. A new , long quality in equities, was Figure 18: Turbulence index – future returns flagged as extremely crowded in the most recent report. This suggests 0.8 that concerns over liquidity risk in the corporate debt market remain 0.6 highly relevant, and we continue to closely monitor this liquidity with 0.4 our credit portfolio managers. We analysed the Hui and Heubel ratio 0.2 for both Bund futures in last quarter’s Market Risk Insights – the ratio 0.0 measures intra-day price movements relative to the ratio of traded -0.2 volume to either market capitalisation or open interest. -0.4 Turbulence index -0.6 Figure 17: The Hui and Heubel ratio for Bund futures -0.8 -1.0 0.10 Russell MSCI MSCI MSCI MSCI 3000 Emerging Emerging Europe China 0.09 Markets Asia 0.08 Full sample annualised return 0.07 Annualised return following most turbulent period Annualised return following most non-turbulent period 0.06 0.05 Source: Hermes, Bloomberg as at 31 March 2016. 0.04

Hui and Heubel ratio 0.03 In Figures 18 and 19 we analyse market turbulence by identifying the 0.02 statistical unusualness of the current risk environment, in terms of both 0.01 volatility and correlation. This demonstrates that most turbulent periods 0.00 precede significant drawdowns in multiple asset classes and markets. Jul 11 Jul 12 Jul 15 Jul 13 Jul 14 Jul 10 Jan 11 Jan 12 Jan 15 Jan 13 Jan 16 Jan 14 Jan 10 Figure 19: Turbulence index – global equities Bund Bund 20-period moving average

Source: Hermes, Bloomberg as at 31 March 2016. 30

Although we witnessed one significant spike in the first quarter, the 25 ratio was generally subdued, suggesting that on the whole liquidity remained at reasonable levels. We would anticipate further spikes 20 throughout 2016, signifying a reduction in , and this demonstration of liquidity risk would be likely to spill over to other, less 15 liquid markets. Turbulence index 10 Concerns about liquidity in the bond markets remain entirely valid in our view, with the distinct possibility for contagion to other asset 5 classes should there be further shocks that lead to capital flight. 0 1999 2001 2003 2005 2007 2009 2011 2013 2015 Event risk Moving average No discussion of risk would be complete without consideration of the events which determine the degree of uncertainty that is Source: Hermes, Bloomberg, MSCI as at 31 March 2016. prevalent at any one time. We recommend the use of non-standard models when attempting to quantify risk, and feel strongly that a Global equity turbulence remained at subdued levels for the first better understanding of possible outcomes stems from stress-testing quarter of 2016. This implies that markets behaved normally relative to portfolios and detailed scenario analysis. their own history, despite the plunge in the early part of the quarter and the subsequent rallies across the board. Times of financial turbulence During the financial crisis, unusually high market volatility and are typically persistent and provide lower rewards for risk than normal turbulence affected the entire global economy. If we can successfully times. As such, this measure can be used to construct portfolios that are identify periods in which asset prices behave uncharacteristically, relatively resistant to turbulence through a conditioning process. then we may be able to minimise portfolio drawdowns by adjusting portfolios appropriately in advance. The tool that allows us to estimate market fragility is the absorption ratio, which captures the market’s ability to absorb shocks. It is best thought of as a measure of systemic risk. We use PCA to determine the extent to which the largest risk factors dominate the entire risk factor set. When markets are particularly vulnerable to shocks, a handful of factors will explain the vast majority of risk, increasing the absorption ratio.

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phenomenon last seen in 1991. This suggests that the recent swings in Figure 20: Absorption ratio – global equities markets were indiscriminate, with investors seemingly caring little about the semantics of stock selection, a cause for concern. 1.00 Event risk is a constant feature of financial markets. Our two key 0.98 metrics for capturing this type of risk, the turbulence index and the absorption ratio, are at odds with one another as we continue through 0.96 2016. Although assets are behaving relatively typically, the markets will remain vulnerable to shocks in the coming months. 0.94

Absorption ratio 0.92 Conclusion In financial markets, we are faced with a core dilemma: while we cannot 0.90 know the probable distributions of asset returns with any certainty, we can be sure that the same outcomes present in ‘normal’ market 0.88 conditions are unlikely to play out during crises. To manage this conflict, 1999 2001 2003 2005 2007 2009 2011 2013 2015 we try to measure risk in our portfolios. Moving average Our assessment of risk during the current quarter suggests the Source: Hermes, Bloomberg, MSCI as at 31 March 2016. following:

Volatility will remain highly changeable and is unlikely to stay at its The absorption ratio was already at elevated levels and moved current low levels. The likelihood of further and more frequent spikes in marginally higher during the quarter. This suggests fragility that the 2016 is significant. other event risk indicators discussed have not identified. Correlation risk appears subdued on the surface, due to short-term We add a new indicator this quarter, termed the smart-money flow decoupling of some asset classes, as the gloss of unconventional index. It is calculated by comparing trading activity in a US equity index monetary policies starts to pale. Should conditions deteriorate, across two time periods, the first and last half hours of the trading day. correlation risk will return to the fore. Activity during the opening spell is dominated by retail investors, buying on emotion and overnight news, while the ‘smart money’ waits until the Stretch risk concerns have subsided, as long-established trends have end of the day when a significantly greater quantity of assets is traded. been broken.

Figure 21: Smart-money flow index Liquidity risk remains a significant issue, particularly in the credit markets. This exacerbates the potential for contagion across asset 20,000 classes in the event of a local shock. 19,500 19,000 Event risk has returned to amber, with one key metric pointing to 18,500 fragility and the other to more subdued levels. We emphasise the 18,000 former over the latter. 17,500 The concerns that overshadowed the markets during the first quarter 17,000 haven’t gone away. China’s slowing growth, increasingly acute political 16,500 risks and uncertainty over the exact path and pace of US rate rises all 16,000 remain problematic. Systematic strategies were highly supportive of the 15,500 recent equity rally contributing to the suppression of realised volatility, 15,000 but that may not be sustained should further market wobbles appear and risk remains skewed to the downside due to the cost of options Jun 11 Jun 12 Jun 15 Jun 13 Jun 14 Sep 11 Dec 11 Sep 12 Sep 15 Mar 11 Sep 13 Sep 14 Dec 12 Dec 15 Dec 13 Mar 12 Dec 14 Mar 15 Mar 13 Mar 16 Mar 14 trading. We are in a fragile risk environment, where markets are capable Smart-money flow index of providing severe dislocations, even if short-lived.

Source: Hermes, Bloomberg as at 31 March 2016. The case for believing that multi-asset portfolios today are more risky than they might initially appear remains a strong one, especially if In the most recent quarter, we saw a significant increase in early activity they rely on historical correlations. Although we have witnessed some relative to activity later in the day. We also recently saw a day on which divergence between equity and bond markets, we believe that the no stock on the New York was at a new 52-week low, a benefits of diversification in portfolios remain over-stated.

The Hermes Investment Office Totally independent of the investment teams, the Hermes investment Office continuously monitors risk across client portfolios and ensures that teams are performing in the best interests of investors. It provides rigorous analyses and attributions of performance and risk, demonstrating our commitment to being a transparent and responsible asset manager

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Contact information Hermes Investment Office Eoin Murray, Head of Hermes Investment Office +44 (0)20 7680 2802 [email protected] Neil Williams, Group Chief Economist +44 (0)20 7680 2398 [email protected]

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Disclaimer This document is for Professional Investors only. The views and opinions contained herein are those of Eoin Murray, Head of the Investment Office, and may not necessarily represent views expressed or reflected in other Hermes communications, strategies or products. The information herein is believed to be reliable but Hermes does not warrant its completeness or accuracy. No responsibility can be accepted for errors of fact or opinion. This material is not intended to provide and should not be relied on for accounting, legal or tax advice, or investment recommendations. This document has no regard to the specific investment objectives, financial situation or particular needs of any specific recipient. This document is published solely for informational purposes and is not to be construed as a solicitation or an offer to buy or sell any securities or related financial instruments. Figures, unless otherwise indicated, are sourced from Hermes. The distribution of the information contained in this document in certain jurisdictions may be restricted and, accordingly, persons into whose possession this document comes are required to make themselves aware of and to observe such restrictions.

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