RiskWatch provides recent data on volatility and correlation, the two components of risk, for U.S. and global equity and fixed-income markets. The equity data are derived from Axioma's medium-term fundamental risk model. The first set of tables is designed to capture U.S. industries, countries and currencies with the highest and lowest levels of volatility and how that component of risk has changed since the end of the prior quarter. The highest and lowest correlated countries within developed and emerging markets are also highlighted. Another chart illustrates how currency volatility has been a major driver of risk in multicountry benchmarks as well as a cause for concern in individual countries. The fixed-income data, detailed in the multiasset-class section, consist of government yields, investment-grade and subinvestment-grade spreads for the U.S. and eurozone as well as correlations among those asset classes. Data are as of September 30, 2016. Change compares to the previous quarter.
| Predicted volatility by industry |
| U.S. market |
|
| Energy equip. & svcs. | 26.1 | -3.2 | | Metals & mining | 22.8 | -2.9 | | Biotechnology | 22.3 | -4.7 | | Airlines | 22.0 | -1.6 | | Oil, gas & consumable fuels | 21.1 | -4.2 | | Ind. power prod. & energy traders | 19.1 | -4.3 | | Marine | 18.2 | -4.8 | | Construction & engineering | 17.6 | -3.4 | | Commercial banks | 17.5 | -4.6 | | Automobiles | 17.4 | -4.8 |
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| Tobacco | 11.9 | -3.5 | | Food & staples retailing | 12.4 | -3.5 | | Household products | 12.4 | -3.5 | | Beverages | 12.8 | -3.8 | | Food products | 12.9 | -3.8 | | Hotels, restaurants & leisure | 13.1 | -3.8 | | Media | 13.2 | -4.1 | | Health care equip. & supplies | 13.2 | -3.7 | | Office electronics | 13.3 | -4.0 | | Health care providers & svcs. | 13.3 | -3.9 |
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| Index volatility |
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| Predicted volatility by currency* |
| Developed markets | |
| New Zealand dollar | 12.3 | -2.0 | | British pound | 12.2 | -1.6 | | Norwegian krone | 11.6 | -1.3 | | Australian dollar | 11.1 | -1.9 | | Japanese yen | 10.5 | -0.8 | | South Korean won | 9.8 | -0.6 | | Canadian dollar | 9.6 | -1.1 | | Swedish krona | 9.2 | -1.5 | | Swiss franc | 8.8 | -1.4 | | Euro | 8.5 | -1.7 |
| Â | | Emerging markets** | |
| South African rand | 19.3 | -2.4 | | Brazilian real | 18.3 | -3.6 | | Colombian peso | 18.0 | -1.0 | | Russian ruble | 17.1 | -3.6 | | Mexican peso | 13.1 | -1.1 | | Polish zloty | 11.0 | -2.0 | | Chilean peso | 10.6 | -0.9 | | Hungarian forint | 10.3 | -2.1 | | Turkish lira | 10.1 | -1.4 | | Malaysian ringgit | 9.3 | -1.7 |
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| Predicted volatility by country* |
| Developed markets |
|
| Japan | 21.0 | -6.1 | | Portugal | 20.4 | -5.7 | | Italy | 20.3 | -5.7 | | Spain | 19.8 | -5.7 | | Ireland | 19.6 | -5.2 |
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| Canada | 12.3 | -4.5 | | South Korea | 12.9 | -3.6 | | New Zealand | 13.1 | -3.8 | | Singapore | 13.5 | -4.6 | | United States | 14.1 | -5.2 |
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| Emerging markets** |
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| Greece | 30.0 | -10.5 | | Egypt | 21.3 | -5.9 | | Turkey | 21.1 | 1.5 | | Peru | 18.9 | -5.9 | | United Arab Emirates | 18.0 | -6.0 |
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| Chile | 11.5 | -3.2 | | Mexico | 11.5 | -2.7 | | Russian Federation | 11.6 | -3.3 | | Czech Republic | 12.1 | -2.7 | | Malaysia | 12.1 | -2.8 |
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| Country-country correlations** |
| Developed markets |
|
| France | Netherlands | 0.82 | | France | Germany | 0.80 | | France | Italy | 0.77 | | France | Belgium | 0.77 | | Spain | France | 0.76 |
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| U.S. | Japan | -0.42 | | U.S. | France | -0.39 | | Spain | U.S. | -0.37 | | U.S. | South Korea | -0.35 | | U.S. | Italy | -0.35 |
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| Emerging markets |
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| Brazil | Peru | 0.35 | | Malaysia | Mexico | 0.34 | | Mexico | Chile | 0.33 | | Czech Republic | Hungary | 0.32 | | Poland | Hungary | 0.29 |
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| China | Mexico | -0.36 | | China | South Africa | -0.34 | | China | Indonesia | -0.30 | | China | Thailand | -0.28 | | China | Chile | -0.28 |
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| Risk vs. return, developed and emerging countries |
| Risk-return tradeoff for both developed and emerging markets as of Sept. 30. The blue bubbles represent developed countries; green bubbles represent emerging countries. Bubble sizes reflect the relative sizes of the markets. The horizontal axis shows the risk forecast from Axioma's short-horizon fundamental model; the vertical axis shows the return for a country over the prior six months. |
| Over time we would expect to see observations going from the lower left quadrant to the upper right (that is, lower return associated with lower risk and higher return with higher risk). But this may not be the case for many shorter periods. For the six months ended Sept. 30, we see this is generally true for emerging markets, while developed markets had similar returns but have widely diverging levels of risk. |
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 | | Multiasset-class data |
| Risk |
|
| U.S. T-note 10-year (yield) | 1.63% | -68 | 67.78% | -0.14% | | U.S. Inv. Grade (spread) | 64 bps | -17 | 23.57% | 0.00% | | U.S. High Yield (spread) | 166 bps | -35 | 30.05% | -0.03% | | Euro Gov't 10-year (yield) | -0.10% | -81 | 60.01% | -0.18% | | Euro Inv. Grade (spread) | 73 bps | 15 | 27.15% | 0.05% | | Euro High Yield (spread) | 126 bps | 2 | 33.12% | 0.09% |
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| Asset-class correlations |
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| U.S. T-note 10-year (yield) | 1.00 | -0.28 | -0.49 | 0.74 | -0.46 | -0.41 | 0.40 | 0.38 | 0.39 | | U.S. Inv. Grade (spread) | | 1.00 | 0.76 | -0.22 | 0.22 | 0.28 | -0.14 | -0.12 | -0.31 | | U.S. High Yield (spread) | | | 1.00 | -0.35 | 0.34 | 0.46 | -0.29 | -0.27 | -0.45 | | Euro Gov't 10-year (yield) | | | | 1.00 | -0.57 | -0.40 | 0.20 | 0.17 | 0.22 | | Euro Inv. Grade (spread) | | | | | 1.00 | 0.84 | -0.13 | -0.09 | -0.20 | | Euro High Yield (spread) | | | | | | 1.00 | -0.19 | -0.16 | -0.34 |
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| U.S. and Euro spread curves are now defined as the spread over the swap curve (previously spread over the government curve). Emerging markets sections include only countries in the FTSE Emerging Markets index. *Numeraire: U.S. dollar. **In excess of the global market. |
| Source: Axioma |
