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Green Bond Issuance Doubled in 2017

A new volatility regime? VIX® don't think so!

ESG Meets Behavioral Finance: Part 1

Mexican Sovereign Debt Structure

Evolving From Single-Factor to Multifactor Investing

Green Bond Issuance Doubled in 2017

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Dennis Badlyans

Associate Director, Global Research & Design

S&P Dow Jones Indices

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Gross issuance of green bonds reached USD 157 billion in 2017, nearly double that of the previous year. Fourth quarter issuance was the fastest quarterly pace on record, adding USD 48 billion, 30% more than seen in each of the previous three quarters.

Issuers and issuance types continue to diversify. Asset-backed security (ABS) issuance had the largest year-over-year increase, accounting for USD 36 billion (23% of gross 2017) of total issuance, up from USD 8.6 billion (8.6% of gross 2016). Development Banks, which historically have been the dominant issuers, issued USD 21 billion (14% of gross 2017), down from USD 24 billion (28% of gross 2016) the previous year. Sovereign issuance, which began with Poland in December 2016, has grown to USD 14 billion as of March 2018, with French Treasury, Fijian, Nigerian, Belgium, and Indonesian sovereign bonds. Hong Kong outlined a grant for first-time green corporate bond issuers and plans to issue the largest amount of green sovereign bonds this year.

Despite steady persistence of issuance from China, the U.S. took the top spot in 2017, driven by the increase in ABS issuance. China, a latecomer to the green bond market, took second place, despite the outsized sovereign issuance by the French government, and held on to its third place spot in total issuance.

USD 113 billion of the primary issuance in 2017 qualified for the S&P Green Bond Index, which is designed to track the global green bond market. The primary inclusion rule for the broad index is price availability—currently, the USD 26.3 billion of Fannie Mae ABS issuance is not being included. Of the bonds included in the broad index, 70% by market value qualified for the S&P Green Bond Select Index. This narrower index further limits inclusion with more stringent financial and extra-financial eligibility criteria (see Exhibit 3).

The S&P Green Bond Select Index can help diversify core fixed income exposure away from treasuries. Despite the ramp up in sovereign issuance, agencies, supras, and local authorities account for the lion’s share of the S&P Green Bond Select Index, representing 60% of the index, while treasury bonds constitute less than 6%. In comparison, core fixed income markets are primarily made up of treasuries. For example, in the Bloomberg Barclays Global Aggregate Bond Index, treasuries make up about 60% of the index.

Investors looking to add an element of green exposure to their core portfolio may be able to replace a portion of their global aggregate bonds with green bonds without sacrificing performance. Despite the differences in composition, historical performance of green bonds has been much like the aggregate index. Over the past year, when regressing the daily returns of the S&P Green Bond Select Index against the Bloomberg Barclays Global Aggregate, there was a 0.91 correlation, with a statistically significant (at 95%) slope of 1.03, and a small positive alpha (see Exhibit 4). That means that market participants looking to green up their portfolio may not need to sacrifice performance to do so.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

A new volatility regime? VIX® don't think so!

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Hamish Preston

Associate Director, U.S. Equity Indices

S&P Dow Jones Indices

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Global equity markets experienced a challenging February.  A U.S.-led selloff triggered a spike in volatility; the Cboe Volatility Index (VIX) recorded its largest ever daily increase on February 5 to reach its highest level since August 2015.

But is higher volatility here to stay?

Towards the end of last year, we published a paper – and a practitioner’s guide – offering a way to convert a VIX level into an estimate for future S&P 500® volatility.  Using the steps outlined in these papers (and a previous blog post), we calculate that given the recent market environment, we might expect the VIX to be around 24.061.  Instead, it closed last night at around two-thirds of that: ending the day at 16.54.

The significant difference between the actual and expected level of VIX suggests that realized volatility may decline at a faster-than-usual rate from its present highs.  In numerical terms, the details of our paper – applied to the present circumstances – tell us that we might anticipate S&P 500 volatility of around 11% (annualized) over the next 30 days2.  Of course, this is a far from perfect prediction; the actual realized volatility of the S&P 500 is extremely unlikely to be exactly as predicted.

Nonetheless, once suitably interpreted, the information encoded in VIX has a moderately impressive record in predicting future changes in volatility.  Despite the recent uptick in volatility, VIX is telling us that market participants are expecting a return to calmer waters.

(1,2) Realized volatility in the S&P 500 over the last 30 days was 19.97% annualized, giving an expected “mean reverted” (MR) volatility of 18.52%.  Adding the expected premium of 5.54 for this level of MR volatility provides an Expected VIX of 24.06.  Subtracting the 7.52% difference between VIX and Expected VIX from the MR volatility of 18.52% provides the expected volatility of 11.00%.  See “Reading VIX: Does VIX Predict Future Volatility?” for more details of both calculations.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

ESG Meets Behavioral Finance: Part 1

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Lauren Smart

Managing Director, Global Head Financial Institutions Business

Trucost, part of S&P Global

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Behavioral economics has had a transformational effect on the fortunes of millions of people saving for retirement through the introduction of auto enrollment, default plans, and “save more tomorrow” schemes. In a series of blogs, I will explore how insights from behavioral economics could be used to revolutionize ESG investing, providing critical levels of capital flows to finance the transition to a more sustainable economy.

‘Nudging’ Sustainable Finance Into the Mainstream: How Behavioral Finance Could Transform Capital Flows to ESG

Richard Thaler won his Nobel Prize for incorporating psychological realism into economic theory, helping transform the pension industry through supposedly irrelevant behavioral “nudges” and making millions of people better off in their old age. He highlighted how human inertia means many people do not join pension schemes, even when employers contribute, essentially turning down “free” money. The rational human of classic economic theory would not behave in this way, but people are not always rational; they are often predictably irrational. Humans prefer the status quo, procrastinate despite best intentions, and underestimate future risks, such as inadequate retirement savings. When these are addressed, for example through auto enrollment, pension participation shoots up. In a recent paper addressing inertia in the Swedish Pension Plan, only 0.9% of people were actively selecting their pension choice, while 99% were on the default plan.[1]

Can We Harness the Power of Inertia to Provide Additional Outcomes for Retirees?

In surveys, millennial market participants are clear that they want more than just a good annuity in retirement. Members enrolling today may not retire for 50 years, during which time the impact of climate change, pollution, and resource scarcity could affect their investments, health, and living environment. The EU taskforce on sustainable finance recommends that pension funds consult beneficiaries on their sustainability preferences and reflect those in their investments;[2] however there is an intent-action gap between what new members say they want and how they actually invest. This can be explained, in part, by inertia, because sustainability funds are usually opt-in. A solution would be for pension funds to default to a sustainability option as NEST, the UK government-backed DC scheme, does. It allocates to UBS’s “Climate Aware World Equity Fund,” which delivers index returns but tilts companies to address climate risks and opportunities. Their rationale is not moral, but rather improved outcomes for members in the face of a green economic transition, because they are “shareholders in that future.”[3] In 2017, HSBC’s UK Pension Scheme transitioned the GBP 1.85 billion equity component of its DC default strategy to LGIM’s “Future World Fund,” a factor-weighted, passive global equity strategy incorporating climate change tilts and exclusions. They echo concern for long-term outcomes for members, “the climate factor tilts [are] especially important as 60% of our members are under 40 years old.”[4] If all schemes defaulted to climate-aware strategies, it could have a profound impact on capital flows to mitigate some of the most damaging financial impacts of climate change and holistically improve outcomes for retirees.

The green elephant in the room is what stands in the way of all pension funds adopting similar approaches. It is a common assumption that sustainability compromises returns; however, this can be traced to cognitive biases such as the “no free lunch” heuristic, confirmation bias, and irrational exuberance about future risks. This is compounded by confusion between “ethical” investing based on values and “sustainable” investing grounded in long-term value generation. Lack of knowledge is another obstacle, both in terms of the pervasive impacts of climate change on asset valuations and an outdated understanding of how sustainability can be incorporated into portfolio construction to deliver myriad risk/return objectives. These topics will be tackled in this blog series and in another recent blog, “Can “Being Green” Deliver Enhanced Returns?

[1]   Cronqvist, Thaler, and Yu (2018), “When Nudges are Forever: Inertia in the Swedish Premium Pension Plan.”

[2]   EU Commission (2018), “Final Report of the High Level Expert Group on Sustainable Finance.”

[3]   National Employment Savings Trust (2017), “NEST responds to climate change.”

[4]   HSBC (2017), “The Best of Both Worlds?

If you enjoyed this content, join us for our Seminar Discover the ESG Advantage in
London on May 17, 2018.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Mexican Sovereign Debt Structure

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Jaime Merino

Director, Asset Owners Channel

S&P Dow Jones Indices

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A couple of months ago, we took a look at the Chilean sovereign bond market and indices. This time, we will analyze the case of Mexico, starting with the local bond market, followed by its structure, and ending with its index performance.

Mexican domestic sovereign debt is issued by the Ministry of Finance (Secretaría de Hacienda y Crédito Público—SHCP) through the Central Bank (Banco de México—Banxico). It is issued through weekly auctions based on the annual finance plan, and on a quarterly basis, the Auction Program of Sovereign Securities is published.

The auctioned securities are:

  1. CETES: Mexican Federal Treasury Certificates are the oldest tradable debt instruments issued by the federal government, issued for the first time in 1978. They are zero-coupon securities that are traded at a discount rate, with a face value of MXN 10 and maturity terms of 28, 91, 182, and 364 days.
  2. MBONOS: Mexican Federal Government Development Bonds with a fixed interest rate are securities issued for terms longer than one year. They pay a coupon every six months, have a nominal value of MXN 100, and have maturity terms of 3, 5, 10, 20, and 30 years.
  3. UDIBONOS: Federal Government Development Bonds, denominated in Investment Units (UDIs), which are inflation linked, were developed in 1996. They are investment instruments that protect the holder from unexpected changes in the inflation rate. UDIBONOS pays a coupon every six months based on a fixed rate plus a gain or loss that is indexed to the performance of the UDI. They have a face value of 100 UDI’s and maturity terms of 3, 10, and 30 years.
  4. BONDES D: Federal Government Development Bonds are instruments that pay floating coupons every 28 days based on the weighted average interbank funding rate, with a maturity term of five years.

Using outstanding amount data, we can see the structure for these four types of bonds with a total of USD 270,000 million (see Exhibit 1). Exhibit 2 shows the maturity profile, including the total per bucket, and we can see that one-third of the total maturities occur between 2019 and 2021. In 2018, without taking into account CETES, USD 32,000 million in bonds are expected to mature between Bondes D and MBonos.

The S&P/BMV Fixed Income Indices have more than 25 different indices, which are mainly divided into maturity buckets, that track the performance of such bonds. Four of them cover the complete curves, tracking more than 170 bonds.  Their performance and annual returns are shown in Exhibits 3 and 4.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Evolving From Single-Factor to Multifactor Investing

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Akash Jain

Associate Director, Global Research & Design

S&P BSE Indices

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This article is the third in a series of blogs. The previous two were titled “Factor Investing 101” and “How Do Single Factors Perform in Different Market Regimes in India?” This blog discusses sectoral tilts of different single factors and  varying correlations between factors in different market cycles.

In our report, sector bias typically existed in factor portfolios, and differentials on sector exposure across factor portfolios were strongly associated with the unique cyclical nature of factor performance. Exhibit 1 highlights the two most overweight and most underweight sectors, on average, for each factor over the period from March 2006 to March 2017. Value and dividend were overweight in basic materials, whereas momentum, quality, and size were overweight in consumer discretionary goods & services. The finance sector was most underrepresented in the momentum, quality, low volatility, and size portfolios, and the information technology sector was underweight in value, dividend, and size portfolios.

Exhibit 1: Sector Bias Versus the S&P BSE LargeMidCap
FACTOR Most Overweight Sectors and Weight Differential (%) Versus Benchmark Most Underweight Sectors and Weight Differential (%) Versus Benchmark
Value Basic Materials, 13.7 Information Technology, -10.9
Energy, 7.7 Fast Moving Consumer Goods, -8.1
Momentum Healthcare, 6.2 Finance, -6.0
Consumer Discretionary
Goods & Services, 5.4
Energy, -5.8
Quality Fast Moving Consumer Goods, 9.6 Finance, -22.9
Consumer Discretionary
Goods & Services, 9.0
Utilities, -4.0
Low Volatility Healthcare, 11.1 Finance, -16.9
Fast Moving Consumer Goods, 6.8 Industrials, -5.2
Dividend Basic Materials, 10.8 Information Technology, -7.0
Energy, 4.2 Healthcare, -4.7
Size Utilities, 4.4 Information Technology, -6.1
Consumer Discretionary
Goods & Services, 4.2
Finance, -5.2

Source: S&P Dow Jones Indices LLC. Data from March 2006 to March 2017. The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Figures in the table are average figures for the semiannually rebalanced portfolios. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance.  Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Despite some single-factor portfolios outperforming the market over the long term, they experienced periods of underperformance in different macroeconomic conditions depending on their cyclical characteristics, as noted in the previous blog. Therefore, blending factors to form multifactor portfolios may potentially help deliver smoother excess return across business and market cycles. Correlation among factors is a common consideration in the construction of multifactor portfolios. However, we observed that factor correlations did not remain constant across various market regimes, and it is important to be mindful of the changes when blending different factors in a portfolio. For example, correlation between size and momentum was negative (-43%) during bull and recovery markets, but switched to positive (32%) in bearish markets. Large shifts in correlation were also observed in the low volatility-momentum and quality-value pairs across different market cycle phases (see Exhibits 2 and 3).

Exhibit 2: Correlation Among Single Factors – Recovery and Bull Market Cycles
FACTOR VALUE MOMENTUM QUALITY LOW VOLATILITY DIVIDEND SIZE
VALUE -42% -38% -50% 86% 83%
MOMENTUM -42% 42% 49% -39% -43%
QUALITY -38% 42% 73% -18% -22%
LOW VOLATILITY -50% 49% 73% -31% -39%
DIVIDEND 86% -39% -18% -31% 82%
SIZE 83% -43% -22% -39% 82%

Source: S&P Dow Jones Indices LLC. Data from October 2005 to June 2017. The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Correlation calculated using excess price returns over S&P BSE LargeMidCap. Index performance based on price return in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Exhibit 3: Correlation Among Single Factors – Bear Market Cycles
FACTOR VALUE MOMENTUM QUALITY LOW VOLATILITY DIVIDEND SIZE
VALUE 11% 13% -20% 78% 59%
MOMENTUM 11% -13% -50% 9% 32%
QUALITY 13% -13% 73% 37% 2%
LOW VOLATILITY -20% -50% 73% 9% -4%
DIVIDEND 78% 9% 37% 9% 53%
SIZE 59% 32% 2% -4% 53%

Source: S&P Dow Jones Indices LLC.  The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Data from October 2005 to June 2017. Correlation calculated using excess price returns over S&P BSE LargeMidCap. Index performance based on price return in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure in the report for more information regarding the inherent limitations associated with back-tested performance.

Please refer to Factor Performance Across Different Macroeconomic Regimes in India for more information on this research paper.

The posts on this blog are opinions, not advice. Please read our Disclaimers.