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Second Quarter 2017 (data as of June 30)

A review of themes and developments The Volatility Paradox: Tranquil Markets May Harbor Hidden Financial markets were exceptionally calm in the second quarter by most measures. Only three times in the past 90 years has volatility been so low: twice during bull markets in the 1960s and 1990s, and once in the lead-up to the financial crisis of 2007-09 (see Figure 1). Is today’s low volatility a sign of calm or a threat to financial stability — or both? This edition of the OFR’s Financial Markets Monitor investigates the volatility paradox: the possibility that low volatility leads to behave in ways that make the financial system more fragile and prone to crisis. We analyze three channels through which a prolonged period of low market volatility may introduce financial stability risks: increased leverage, reduced hedging, and institutional investors’ use of -management models. We find some supportive evidence of these channels at work, but better data are needed to make definitive conclusions. Volatility alone is not a good indicator of impending financial stress.

Figure 1: S&P 500 Index 90-day Realized Volatility (percent) Volatility in U.S. equity markets is the lowest in decades 75

6.47 on May 14, 2017

7.12 on Jan. 2, 2007

50 6.45 on May 2, 1995

4.01 on April 23, 1964

25

0 1928 1938 1948 1958 1968 1978 1988 1998 2008 2018 Note: Volatility is the of daily returns over 90 days expressed as annualized percent change. Source: Bloomberg L.P.

Key findings

• Volatility for most asset classes across the world fell below historical averages during the second quarter. In some cases, volatility is near all-time lows. Drivers of low volatility may include expectations that the U.S. economic expansion and still-easy funding conditions will persist.

• Some institutional investors have adapted by increasing leverage and the use of -enhancing strategies.

• Shocks could produce procyclical responses if market participants use measures of realized volatility to manage the risk of their portfolios.

This monitor reflects the best interpretation of financial market developments and views of the staff of the Office of Financial Research (OFR). It does not necessarily reflect a consensus of market participants or official positions or policy of the OFR or the U.S. Department of the Treasury. Contributors: Meraj `Allahrakha, Viktoria Baklanova, Danny Barth, Ted Berg, Jill Cetina, Dagmar Chiella, Arthur Fliegelman, Dasol Kim, Francis Martinez, John McDonough, Philip Monin, Mark Paddrik, Eric Parolin, and Daniel Stemp.

Volatility alone is a weak risk indicator. Figure 2: Realized Volatility by Asset Class (z-score) Volatility has declined across major asset classes and markets Volatility measures for most asset classes across 9 U.S. equities global financial markets fell below their historical U.S. interest rates averages during the second quarter (see Figure 2). 6 Global currencies Some measures approached all-time lows (for Global equities (ex-U.S.) example, see Figures 1 and 3), which may have been 3 driven by expectations that the long U.S. economic expansion and still-easy funding conditions will 0 persist.

-3 There are two types of volatility: realized and implied. 2007 2010 2013 2016 Realized volatility reflects the historical price Note: Realized volatility is the standard deviation of daily returns over 30 days, fluctuations of an asset. is forward- expressed as annualized percent change. U.S. equities are represented by the S&P 500 index. U.S. interest rates are the weighted average of the Treasury yield curve. looking. It captures the market’s expectation of Global currencies are based on weights from JPMVXY index. Global equities are future price fluctuations of an asset, derived from the MSCI All Countries World Excluding U.S. Index. Standardization uses data since Jan. options markets. 1, 1993. Sources: Bloomberg Finance L.P., OFR analysis

When implied volatility exceeds realized volatility, the Figure 3: Chicago Board Options Exchange Volatility Index (VIX) difference reflects the extra return investors demand (percent) to hold a security solely because it is volatile. This Implied volatility on equities has fallen to near all-time lows 100 difference is known as the volatility . 80 One of the most widely cited measures of implied 9.31 on Dec. 22, 1993 9.75 on June 2, 2017 volatility is the Chicago Board Options Exchange 60 Volatility Index (VIX). The VIX is the 30-day implied volatility of options on the benchmark S&P 40 500 equity index. A low VIX doesn’t necessarily signal that severe financial stress is unlikely. For 20 instance, the VIX provided no advance warning of extreme volatility in the months leading up to the 0 1990 1995 2000 2005 2010 2015 financial crisis. Realized volatility of the S&P 500 Note: Implied volatility is derived from options markets and is the expected standard index was often substantially higher than the VIX had deviation of daily returns over the next 30 days, expressed as annualized percent change. predicted 30 days earlier (represented by the blue dots Source: Bloomberg Finance L.P. over the 45-degree line in Figure 4). The relationship between realized and implied volatility for other asset Figure 4: VIX and Realized Volatility of S&P 500 Index (percent) classes followed a similar pattern during the crisis. The VIX did not predict the global financial crisis 100 Realized Market risks may seem low when volatility is low. volatility However, low volatility may also serve as a catalyst 75 for market participants to take more risk, thereby making the financial system more fragile. This phenomenon is known as the volatility paradox. 50 Global financial crisis (8/1/2008 to 11/1/2008) Low volatility directly incentivizes risk-taking. 25 Other dates (3/1/1993 to 5/31/2017) VIX Lower volatility may contribute to greater leveraging 0 and risk-taking through at least three channels. The 0 25 50 75 100 first channel is through changing asset-return Note: Realized volatility is the standard deviation of daily returns over 30 days, expressed as annualized percent change. correlations, which tend to increase when markets are Sources: Bloomberg Finance L.P., OFR analysis volatile. Low correlations could entice investors to

OFR MARKETS MONITOR Second Quarter 2017 | 2 accumulate risky exposures, believing they are Figure 5: 3-Month of S&P 500 Sector diversified. Prolonged periods of low volatility may Correlations, VIX Index (correlation, percent) further decrease correlations, encouraging further Correlations between sectors have fallen amid low -taking. This procyclical behavior increases 1.00 Sector correlation (left) 75 investors’ risk of loss from a systematic shock, when VIX index (right) volatility spikes and asset-return correlations revert to 0.75 historical levels. 50

Some evidence exists that this channel may be at 0.50 work in equity markets. Sector correlations have 25 declined significantly during the past two years, while 0.25 volatility has remained low (see Figure 5).

0.00 0 Second, low volatility could encourage the use of Jan Jan Jan Jan Jan Jan Jan other yield-enhancing strategies, such as selling deep 2005 2007 2009 2011 2013 2015 2017 out-of-the-money put options (those with a strike Note: S&P 500 sector pairwise monthly correlation; 3-month moving average. price substantially below current prices). Investors Sources: Bloomberg L.P., OFR analysis collect a premium from selling these options, but can Figure 6: Debt Balance over and be obligated to purchase the underlying assets if the S&P 500 Index 30-day Realized Volatility (percent) price drops below the . Investors who Realized volatility has fallen as investors increased margin debt accumulate these risky exposures could be more 3 100 Margin debt / market capitalization (left) likely to experience financial stress if prices sharply S&P 500 index realized volatility (right) decline. Available data on portfolios are not 75 sufficient to assess this channel adequately. 2 50 Third, low volatility can directly incentivize leveraging by lulling investors into underestimating 25 the odds of a volatility spike. One measure of marketwide leverage is the ratio of margin debt to 1 0 market capitalization. This measure is imperfect Jan Jan Jan Jan Jan Jan Jan 2005 2007 2009 2011 2013 2015 2017 because it doesn’t account for other positions on Note: Values are the New York Exchange (NYSE) market capitalization and investor balance sheets, including derivatives margin debt balances of its members. Dealer margin debt balances may reflect positions. Figure 6 uses margin debt balances and positions on securities not listed on the NYSE. Realized volatility is the standard deviation of daily returns over 30 days expressed as annualized percent change. market capitalization data from the New York Stock Sources: Haver Analytics, OFR analysis Exchange. The ratio increased from 2002 to 2007 amid low volatility, declined after the crisis, and has been climbing since as volatility again reached long- term lows.

Evidence also exists that some large investors are highly leveraged and, for that reason, may be susceptible to volatility events. For example, the top decile of macro and relative-value hedge funds has been leveraged about 15 times in recent quarters. These funds combined account for more than $800 billion in gross assets, about one-sixth of all assets. Low volatility could also disincentivize investor hedging.

OFR MARKETS MONITOR Second Quarter 2017 | 3 Another way investors may adapt to low volatility is Figure 7: SPY Options Held for Hedging Purposes (percent) by reducing their hedging of risky positions. This Investors are less hedged compared to the pre-crisis period behavior was particularly relevant in recent years, 100 when historically low interest rates pressured Put hedging rate investors to reach for yield by holding more lower- Call hedging rate rated fixed-income securities and more equities (see 90 the OFR’s 2016 Financial Stability Report). OFR analysis of options trading suggests that investors have reduced their hedging of market exposure. 80 Investor hedging activity is difficult to measure, although it can be captured to some extent using contracts outstanding in current-month SPY options. 70 SPY is an exchange-traded fund that mirrors the benchmark S&P 500 equity index. Traders commonly sell SPY options to hedge equity market exposure. 60 2005 2007 2009 2011 2013 2015 2017 Options give investors the right, but not the obligation, to buy or sell a specific security at a Sources: OptionsMetrics, OFR analysis specific strike price and time. A call is a right to buy; a put is a right to sell. Figure 8: VIX Futures Noncommercial Net Total (contracts) Options with a strike price near the current price of Speculators increased bets on VIX to the most since 2004 SPY are said to be “at the money.” Contracts with a 50,000 strike price far from the current price are “away from Net the money.” These options are less likely to be held for hedging purposes and instead may represent 0 yield-enhancing strategies. Investor hedging activity is captured through a hedging rate, calculated as the proportion of contracts on SPY options that is “at -50,000 the money” versus “away from the money.” Hedging rates are currently lower on average than in the years immediately preceding the financial crisis (see Figure -100,000 7), suggesting a structural change in hedging activities after the crisis. However, the evidence is somewhat -143,845 on June 20, 2017 mixed. Considerable variation has occurred since -150,000 2010, and current levels appear to be higher relative 2004 2006 2008 2010 2012 2014 2016 to 2014 for both call and put hedging ratios. The Sources: Bloomberg Finance L.P., Commodity Futures Trading Commission absence of sharper measures of aggregate hedging activities makes drawing definitive conclusions difficult, though these hedging ratios at least suggest significant differences before and after the crisis.

The Commodity Futures Trading Commission (CFTC) collects data on an alternative measure of hedging activity using positions of futures traders. CFTC data categorize hedge funds and other investors as “non-commercial,” or speculative, traders. As of May 2017, the net short position on VIX futures of non-commercial traders sat at levels larger than even before the crisis (see Figure 8). Common volatility strategies involve taking short positions in longer-dated contracts and long positions in shorter-dated contracts. Reduced

OFR MARKETS MONITOR Second Quarter 2017 | 4 hedging in these strategies would imply shorting in Figure 9: Cumulative Yield Change in 10-year Government Bonds the aggregate, consistent with Figure 8. However, (basis points) establishing a direct link without more granular data VaR shocks may have deepened past selloffs in bond markets is difficult. 0 Japanese Government (6/12/2003 to 9/10/2003) Together, these data suggest that some investors may 25 U.S. Treasuries taper tantrum have adapted to the low-volatility environment by (5/2/2013 to 7/31/2013) reducing risk hedges and increasing speculative bets. 50 Data limitations temper the findings to some extent, and leave opportunities for further analysis. With less 75 hedging, these investors’ balance sheets may be less resilient to large volatility shocks when volatility 100 returns to financial markets. 125 Day 0 Day Day Day Day Day Day Day Day Day Value-at-Risk models may give faulty signals in 10 20 30 40 50 60 70 80 90 Note: The vertical axis is inverted to reflect lower bond prices as yields increase. low-volatility markets. Horizontal axis is the number of days since the beginning of the sell-off period. Sources: Bloomberg Finance L.P., OFR analysis Low realized volatility can affect the behavior of banks, hedge funds, and other asset managers that use a risk management framework based on realized volatility, including some Value-at-Risk (VaR) measures. About 40 percent of large hedge funds, representing about 62 percent of gross hedge fund assets, regularly calculate VaR statistics for their funds, according to Form PF data collected by the Securities and Exchange Commission (SEC).

VaR measures the risk of . It captures how much value investments might lose over a set time. Although VaR can be a valuable risk- management tool, overreliance on VaR when volatility is low could result in procyclical behavior that makes investors more vulnerable to volatility shocks if market conditions change abruptly.

A decline in realized volatility can reduce a portfolio’s VaR, allowing market participants to increase position sizes without exceeding predefined VaR risk limits. The reverse is true when volatility rises. In that case, VaR-sensitive investors may be forced to simultaneously sell assets to get their portfolios below risk limits.

A selloff induced by a VaR shock can become self- reinforcing as liquidity dries up and as deleveraging occurs. Some market observers believe VaR shocks contributed to selloffs in the Japanese market in 2003 and in the U.S. Treasury market during the 2013 taper tantrum (see Figure 9). Long- term investors that are not sensitive to VaR, such as pension funds and insurance companies, may not step in and provide liquidity unless prices fall sharply.

OFR MARKETS MONITOR Second Quarter 2017 | 5

Figure 10: U.S. G-SIBs' Combined Trading Books ($ billions) Most large U.S. banks report data on the VaR of their Big banks' VaR has collapsed but portfolio size is little changed trading books in quarterly 10-Q filings to the SEC. These data show a dramatic decline since 2010 in the 2,000 1.00 VaR of banks’ trading books, without a commensurate decrease in the fair value of those 1,500 0.75 trading books (see Figure 10). All else being equal, this change suggests that the reduction in VaR may 1,000 0.50 reflect falling realized volatility rather than a decline in the size of banks’ trading books during the period. 500 0.25 If volatility rises and banks aim to keep their VaR Fair value of trading book (left) stable, the banks would need to shrink their trading Trading book VaR (right) books. Another possibility is that the declining VaR 0 0.00 is evidence that banks have reduced the overall Mar Mar Mar Mar Mar Mar Mar Mar market risk in their portfolios, in part responding to 2010 2011 2012 2013 2014 2015 2016 2017 additional regulatory oversight. A definitive Note: G-SIB = Global systemically important bank Sources: Bank 10-Q forms filed with Securities and Exchange Commission, OFR conclusion is difficult without detailed data on dealer analysis positions. Figure 11: Variable Annuity Volatility Control Funds ($ billions, Targeting a specific level of volatility has recently count) become an strategy. Many institutional Variable annuity volatility control funds have more than doubled investors now are holding so-called “volatility control in size 300 400 funds” in their portfolios. Assets under management Number of funds (left) in variable annuity volatility control funds rose to Assets under management (right) $325 billion at the end of 2016 (see Figure 11). These 300 funds make asset allocation decisions aimed at 200 maintaining a stable level of volatility for their whole 200 portfolios. If volatility were to rise suddenly in a previously stable asset class, these funds may be 100 forced to rebalance and sell assets. These investors’ 100 activities could have a procyclical effect on asset prices and exaggerate volatility. 0 0 Dec Dec Dec Dec Dec Dec Dec 2010 2011 2012 2013 2014 2015 2016 Conclusion Source: Milliman Financial Risk Management LLC

Prolonged low market volatility may introduce financial stability risks through at least three channels. First, investors could respond by directly taking on more leverage and risk. Second, investors could reduce hedging activities. Third, institutional investors’ use of VaR or other risk-management models that have realized volatility as a key input could lead them to take more risk. A spike in volatility can result in outsized investor losses from sharp asset price changes. Data limitations hinder the ability to make definitive conclusions regarding the extent to which these channels are at work. However, the evidence is consistent with these channels operating and suggests the need for further analysis.

OFR MARKETS MONITOR Second Quarter 2017 | 6 Selected Global Asset Price Developments

OFR MARKETS MONITOR Second Quarter 2017 | 7 Select U.S. Interest Rates

U.S. T reasury yields and yield curve U.S. T reasury term premium (basis points) percent basis points 2-year (Left Axis) 10-year 2-year 3 250 10-year (Left Axis) 300 10-year - 2-year spread (Right Axis)

225 250

200 200 2 150 175 100

150 50 1 125 0

100 -50

-100 0 75 2010 2011 2012 2013 2014 2015 2016 2017 Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 Note: Adrian, Crump, & Moench model Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

Professional vs. market-implied U.S. expectations Short-term market rates (percent) (percent ) 1-month Treasury bill 3-month LIBOR Survey of Professional Forecasters 10-year annual average GCF Treasury Repo 1.4 5y5y forward breakeven rate CPI current 3.0 1.2 2.5 1.0 2.0 0.8

1.5 0.6

1.0 0.4

0.5 0.2

0.0 0.0

-0.5 -0.2 Jul Sep No v Jan Mar May Jul Sep No v Jan Mar May Jul Sep No v Jan Mar May Jul 2016 2016 2016 2017 2017 2017 2017 2015 2015 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 S ource: Bloomberg Finance L.P. S ource: Bloomberg Finance L.P.

T hree-month Eurodollar futures (percent) Money market and policy interest rates (percent) Jun-30 May-17 Apr-17 1.5 FOMC projections from June 2017 GCF Treasury repo 4.5 Fed funds effective Interest on excess reserves 4.0 1.25 Reverse repo facility

3.5

3.0 1.0

2.5 0.75 2.0

1.5 0.5 1.0

0.5 0.25

0.0 Jul Jan Jul Jan Jul Jan 2017 2018 2018 2019 2019 2020 0.0 Notes: T he high and low points of the Dec FOMC projections are the maximum 2011 2012 2013 2014 2015 2016 2017 and minimum forecasts. T he rectangle represents the median. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

1 U.S. Corporate Debt Markets

U.S. corporate -adjusted spreads U.S. corporate CDS indexes (basis points) (basis points) Investment grade (Left Axis) High yield (Right Axis) Investment grade (Left Axis) High yield (Right Axis) 150 600 240 1000

220 120 500 800 200 90

180 400 600 60 160

140 300 400 30

120

0 200 100 200 Jul O ct Jan Ap r Jul Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 2016 2016 2017 2017 2017 Note: Five-year maturity CDS Index S ource: Haver Analytics Source: Bloomberg Finance L.P.

U.S. non-financial credit gross issuance ($ billions) U.S. corporate credit fund flows ($ billions) Investment Grade High Yield Leveraged Loans High yield Leveraged loans

150 10

5

100 0

-5

50

-10

-15 0 Jul O ct Jan Ap r Jul Aug O ct D ec Feb Ap r Jun Aug O ct D ec Feb Ap r Jun 2016 2016 2017 2017 2017 2015 2015 2015 2016 2016 2016 2016 2016 2016 2017 2017 2017 Note: Flows data are released with one-month lag. Sources: Dealogic, Standard & Poor's Leveraged Commentary & Data S ource: Haver Analytics

Leveraged loan issuance by use of proceeds (percent) Leveraged loan price activity M&A/LBO /Buyback Refinancing Other 110 100

90

80

70 105

60

50

40 100 30

20

10

0 95 2000 2002 2004 2006 2008 2010 2012 2014 2016 Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 Note: Data for 2017 are year-to-date as of January. Notes: S&P Leveraged Loan Index. Index 100=January 01, 2012. Sources: Standard & Poor's Leveraged Commentary & Data, OFR analysis Source: Bloomberg Finance L.P.

2 Primary and Secondary Mortgage Markets

Primary mortgage rates (percent) MBS yield and option-adjusted spread to U.S. T reasury 5-year/1-year adjustable rate 30-year fixed securit ies Current coupon (Left Axis) Spread (Right Axis) 5 percent basis points 4 120

4 100 3

80

3 2 60

2 1 40 Jul O ct Jan Ap r Jul Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 2016 2016 2017 2017 2017 Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

30-year home mortgage fixed and jumbo rates and spread Conventional mortgage severe delinquencies percent basis points (percent, 90+ days late, seasonally adjusted) 5.5 30-year fixed (Left Axis) 120 Prime Subprime 30-year jumbo (Left Axis) 16 30-year jumbo-conforming spread (Right Axis) 100 5.0

12 80 4.5

60 8 4.0 40

4 3.5 20

3.0 0 0 Jul O ct Jan Ap r Jul 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2016 2016 2017 2017 2017 Source: Bloomberg Finance L.P. S ource Haver Analytics

Refinance and purchase loan applications Purchase Index (Left Axis) Gov Refi Index (Left Axis) Conv Refi Index (Left Axis) Refi % of total apps (Right Axis) percent 240 70

65 190 60

140 55

50 90 45

40 40 Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 Note: Index 100 = July 01, 2016. Source: Bloomberg Finance L.P.

3 Equity Markets

Global equity indices U.S. equity indexes S&P 500 MSCI EM Nikkei 225 S&P 500 Russell 3000 Shanghai Euro Stoxx 50 135 180

125 140

115

100

105

60 95

85 20 Jul O ct Jan Ap r Jul 2000 2003 2006 2009 2012 2015 2016 2016 2017 2017 2017 Note: Index = July 01, 2016. Note: Index 100 = Jan 01, 2000. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

S&P 500 sector performance S&P 500 price-to-earnings and price-to-book ratios S&P 500 Financials Consumer staples (mult iple) Energy Price-to-earnings (Left Axis) Price-to-book (Right Axis) 140 21 4

130

120 17 3

110

100 13 2

90

80 Jul O ct Jan Ap r Jul 9 1 2016 2016 2017 2017 2017 2012 2014 2016 Note: Index 100 = July 01, 2016. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

U.S. equit y valuat ions: Shiller CAPE (rat io) S&P 500 implied volatility and option skew (percent ) 50 50 VIX 80% - 120% Skew

30 40 40

30 30 20

20 20 10

10 10

0 Jul O ct Jan Ap r Jul 0 0 2016 2016 2017 2017 2017 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Notes: Option skew is the difference between three-month implied volatility of out of the money puts and calls with strikes equal distance from the spot price Notes: CAPE is the ratio of the monthly S&P 500 price level to trailing ten-year (+/- 20 percent). Higher values reflect greater demand for downside risk average earnings (inflation adjusted). protection. Sources: Haver Analytics, Robert Shiller Source: Bloomberg Finance L.P.

4 Volatility

Implied volatility by asset class (Z-score) Realized volatility by asset class (Z-score) U.S. equities (VIX) Global currencies (JPMVXYGL) Global FX U.S. interest rates U.S. equities U.S. Treasuries (MOVE) Average Average 3 3

2 2

1 1

0 0

-1 -1

-2 -2

-3 -3 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 Notes: T hirty-day realized volatility. Equities based on S&P 500 index, interest Notes: Z-score represents the distance from the average, expressed in rates based on weighted average of T reasury yield curve, FX based on weights standard deviations. Standardization uses data going back to January 01, 1993. from JPMVXY index. Standardization uses data going back to January 01, 1993. Sources: Bloomberg Finance L.P., OFR analysis Sources: Bloomberg Finance L.P., OFR analysis

Global equity indexes 1-month implied volatility (percent) Slopes of implied volat ilit y curves Eurostoxx 50 SP500 German Dax (basis points) UK FTSE 300 Japan Nikkei MSCI EM 60 G10 FX (Left Axis) S&P 500 (Left Axis) 2-year USD Swaption Rate (Right Axis) 800 40 10-year USD Swaption Rate (Right Axis)

50 600 30

400 20 40 200 10

30 0 0

-200 -10 20

-400 -20

10 -600 -30 Jul Sep No v Jan Mar May Jul 2016 2016 2016 2017 2017 2017 2017 0 Notes: Seven-day moving average. Slope represents difference between one- Jul Sep No v Jan Mar May Jul Sep No v Jan Mar May Jul year and one-month maturities. G10 FX based on weights from Deutsche Bank's 2015 2015 2015 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 CVIX index. Source: Bloomberg Finance L.P. Sources: Bloomberg Finance L.P., OFR analysis

Option skew by asset class (z-score) Volat ilit y of equit y volat ilit y U.S. equities U.S. interest rates 140 Global currencies Average 3

2 120 1

0

100 -1

-2

-3 80 Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 Notes: Option skew is the difference between three-month implied volatility of out of the money puts and calls with strikes equal distance from the spot price (+/- 10 percent). Higher values reflect greater demand for downside risk 60 protection. Equities represents S&P500 index. Interest rates represent Jul O ct Jan Ap r Jul weighted average skew of T reasury futures curve. Currencies represent dollar 2016 2016 2017 2017 2017 skew against major currencies based on JPMVXY index weights. Z-score Note: VVIX Index measures the expected volatility of the 30-day standardization uses data going back to January 01, 2006. of the CBOE VIX Index. Sources: Bloomberg Finance L.P., OFR analysis Source: Bloomberg Finance L.P.

5 Advanced Economies

2-year sovereign bond yields (percent) 10-year sovereign bond yields (percent) U.S. Germany U.K. Japan U.S. Germany U.K. Japan France

2 3.0

1.5 2.5

1 2.0

0.5 1.5

0 1.0

-0.5 0.5

-1 0.0

-1.5 -0.5 Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 Jul O ct Jan Ap r Jul Source:2016 Bloomberg Finance2016 L.P. 2017 2017 2017 Source: Bloomberg Finance L.P.

Breakeven inflation (percent) 10-year euro area periphery government bond spreads U.S. ten-year Germany ten-year U.K. ten-year over German bunds (basis points) Japan ten-year Italian govt (Left Axis) Spanish govt (Left Axis) 4 Portuguese govt (Left Axis) Greek govt (Right Axis) 400 2000

360

3 1600 320

280 1200 2 240

800 200

1 160 400 120

0 80 0 Jul O ct Jan Ap r Jul Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 2016 2016 2017 2017 2017 Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

Major currency indexes U.S. dollar long posit ioning vs. major currencies DXY (U.S. dollar) euro British pound (net speculative positions, thousands of contracts) Japanese yen Swiss franc DXY (U.S. dollar) euro British pound 110 Japanese yen Total 400

300

100 200

100 90

0

-100 80 Jul O ct Jan Ap r Jul Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 2016 2016 2017 2017 2017 Notes: Positive values represent net U.S. dollar long positions. T he Dollar Notes: Foreign currency increases represent greater strength versus the U.S. Index (DXY) is a based on the U.S. dollar's value against a dollar. DXY increases represent greater strength of the U.S. dollar versus a basket of major world currencies. T o express a U.S. dollar long position in a basket of major world currencies. Index 100 = July 01, 2016. non-U.S. dollar contract, the contract must be shorted. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

6 Emerging Markets

Emerging market currencies Emerging market sovereign debt yield (U.S. dollars per foreign currency unit ) EM local currency

120 Russia 5.5 China offshore Brazil EM currency Index

5.0 110

4.5

100

4.0

90 Jul O ct Jan Ap r Jul 2016 2016 2017 2017 2017 3.5 Notes: Increasing values indicate strengthening versus the U.S. dollar. Index Jul O ct Jan Ap r Jul 100=July 01, 2016. 2016 2016 2017 2017 2017 Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

Equit y price indexes 1-month realized emerging markets volatility (percent) EM sovereign hard currency debt EM currencies 130 U.S. China Developed economies Emerging markets 20

120

110

10 100

90

80 0 Jul O ct Jan Ap r Jul 2010 2011 2012 2013 2014 2015 2016 2017 2016 2016 2017 2017 2017 Notes: T he US equity index is the S&P 500 Index. T he Chinese equity index is Notes: Realized volatility is the annualized standard deviation. Hard currency the Shanghai Composite Index. T he Developed Economies index is the MSCI sovereign debt based on the J.P. Morgan Emerging Bonds - Global Price Index World Index and the Emerging Markets index is the MSCI EM Index (both are in and currencies based on a weighted average of EM currency returns against the local terms). Index 100 = July 01, 2016. dollar using weights from J.P. Morgan VXY-EM currency volatility index. Source: Bloomberg L.P. Sources: Bloomberg L.P., OFR analysis

IIF portfolio flows to emerging markets ($ billion) China's Foreign Exchange Reserves ($ t rillion) Debt flows Equity flows $4.5 FX reserves 50

$4 40

30 $3.5

20 $3 10 $2.5 0

-10 $2

-20 $1.5

-30 2010 2011 2012 2013 2014 2015 2016 2017 $1

Notes: Data represent the Institute of International Finance's monthly estimates of non-resident flows into thirty EM countries. Data for latest observations are $0.5 derived from IIF's empirical estimates using data from a smaller subset of Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan countries, net issuance, and other financial market indicators. 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Source: Bloomberg Source: Bloomberg

7 Commodities

Major commodities prices Crude oil Bloomberg commodities index $/Barrel WTI (Left Axis) Million Barrels Crude oil front month (Brent) Gold front month 140 Brent (Left Axis) 550 125 U.S. inventories (Right Axis)

500 100 110

75 450

80

50 400

50 25 350

0 20 300 2014 2015 2016 2017 2014 2015 2016 2017

Notes: Index 100 = January 01, 2010 Note: WT I and Brent are front-month contracts. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

Oil and natural gas futures curves Oil supply and demand factors Brent (Left Axis) Natural gas (Right Axis) Million barrels per day Global production (Left Axis) $/barrel $/mmbtu 120 1800 Global consumption (Left Axis) 60 4.0 U.S. rig count (Right Axis) 115 1600

1400 110 3.5 1200 55 105 1000 3.0 100 800

50 95 600 2.5 90 400

85 200 45 2.0 2014 2015 2016 2017 2M 6M 12M 24M 60M Note: Global production and consumption are estimates by the International Note: Data as of July 05, 2017. Ene rgy Age ncy. Sources: Bloomberg Finance L.P., OFR analysis Source: Bloomberg Finance L.P.

Speculative futures positioning Metals spot price indexes (thousands of contracts) Copper Steel Precious metals Brent (Left Axis) Gold (Left Axis) 200 Copper (Left Axis) Steel (Right Axis) 350 50

280 40 150 210 30

140 20 100 70 10

0 0 50 -70 -10

-140 -20 Jul Jan Jul Jan Jul 0 2015 2016 2016 2017 2017 Jul Jan Jul Jan Jul Notes: Positive values represent net long positions. Negative values represent 2015 2016 2016 2017 2017 net short positions. Note: Index 100 = January 01, 2010. Source: Bloomberg Finance L.P. Source: Bloomberg Finance L.P.

8