Indexology^® Blog

This Fall has been a difficult time for property & casualty companies.  The fires in Northern California have destroyed thousands of homes and the relentless string of hurricanes have damaged parts of Texas, Louisiana, Florida, Puerto Rico and the U.S. Virgin Islands.

If these companies need to sell fixed income assets to offset liabilities they could impact the U.S. municipal and corporate bond markets.  While both of these markets have seen high demand during 2017, a secondary market influx of bonds may be a catalyst that begins to push yields higher.

A quick look at data from the Federal Reserve shows the potential. With over $300billion of municipal bonds and over $400billion of corporate and foreign bonds held by these companies shows the pool of assets these companies could tap to offset liabilities is significant.

Munis: While it may appear the most vulnerable, the municipal bond market has had it’s supply / demand equilibrium out of kilter for some time due to low new issuance.  The open question would be can it handle a wave of potential selling?

Corporate bonds: Again, U.S. and foreign demand for USD corporate bonds remains strong. Untested all year, this asset class has not seen any real selling pressure to benchmark against.

Table 1) Select asset classes held by property & casualty companies:

Meanwhile, the S&P National AMT-Free Municipal Bond Index (Investment grade) has recorded a 4.89% total return this year and the S&P 500/MarketAxess Investment Grade Corporate Bond Index has returned 5.59% return year-to-date (October 13, 2017.)

Dividend investing is an ever popular topic across different markets.  Market participants have been using ETFs to implement various dividend strategies for more than a decade.  The first dividend ETF, the iShares Select Dividend, was launched in 2003.  Nevertheless, dividend ETPs have experienced tremendous growth in assets since year-end 2009.  As of June 30, 2017, the total assets of purely dividend screened or weighted ETPs reached USD 178.6 billion, with a compound annual growth rate of 35.5% since year-end 2009 (see Exhibit 1).

Despite rising AUM in the low/minimum volatility and multi-factor ETPs in the past two years, dividend ETPs have remained the leading category of strategic-beta ETPs by assets across many regions and countries.^[1]  In the first three quarters of 2017, dividend ETPs dominated the inflows among various types of smart beta ETPs.[2]

The most cited growth driver for dividend investing is the global low-yield investment environment seen in recent years.  As shown in Exhibit 3, the growth of dividend ETPs’ assets since year-end 2009 coincided with a period of low and declining 10-year government bond yields in the U.S., eurozone, and Japan.

Another underlying and probably longer-term driver for the growth of dividend investing is the demographic change in high income regions, such as the U.S., European Union, and Japan (see Exhibit 4).  As more market participants move gradually from the consolidation to the decumulation stage of their investment lifecycles, dividend strategies can become an attractive investment option for providing stable cash flows.

Among various types of income ETPs listed in the U.S., high-dividend equity ETPs recorded the highest five-year absolute and risk-adjusted return as of Aug. 31, 2017, although they had lower yield than a few other income asset classes.  Compared to other equity income products, such as REITs and MLP ETPs, high-dividend equity ETPs tend to have less sector concentration risk and lower price volatility.  Despite the rate hikes since December 2015, U.S. interest rates remain low and high-yield ETPs, which offer yield premium, remain attractive to income-seeking market participants.

[1]   A Global Guide to Strategic-Beta Exchange-Traded Products (September 2016 and September 2017).  Morningstar Manager Research.

[2] Blackrock Global ETP Landscape September 2017 Report.

It is interesting and amazing to see how people react to natural disasters.  Whether it be a hurricane, flood, tsunami, earthquake, and no matter where the disaster is located, the whole world takes notice and helps with anything they can.  This was certainly the case for Mexico after the 7.1 magnitude earthquake that shook the capital on Sept. 19, 2017 (12 days after an 8.2 earthquake in the country, and exactly 32 years later after the most significant and deadliest earthquake to hit Mexico City), where thousands of Mexicans took to the streets to help in some way.  Troops from around the world (Chile, China, Colombia, Costa Rica, Ecuador, El Salvador, Spain, Guatemala, Honduras, Israel, Japan, Panama, and the U.S., among others) collaborated in search and rescue efforts in the aftermath.  We, and I believe all Mexicans, are very grateful to the people that helped and are still helping—thanks so much to all of you.

But the most amazing part is that the generosity was not only that of the people, but companies from sectors like telecommunciations and transportation also helped for eight days by not charging fees for internet, telephone services, public transportation, and many toll roads, and this contributed to a 0.31% drop in year-over-year inflation for September 2017 after 14 consecutive months of increases.  Exhibit 1 shows the history of annual CPI over the past 10 years.

Exhibit 2 shows the performance of Mexican inflation-linked bond indices in different periods, and we can see how in the one-month window, the short-term end of the curve saw gains, while losses were observed in the middle and long term.

If you were wondering how inflation behaved after the 1985 earthquake, Exhibit 3 shows inflation from 1980-1990, where we can see that inflation rose for 15 consecutive months after the earthquake—but it’s difficult to make a conclusion based on that data, since during that time Mexico had severe hyperinflation problems.

Now that we have talked about natural disasters, there is an imminent “natural disaster” with the NAFTA negotiations.  Over the past 22 days (from the Sept.19, 2017, earthquake through Oct. 11, 2017, as the fourth round of negotiations begins) the Mexican peso depreciated 5.37% (almost MXN 1) against the U.S. dollar.  Exhibit 4 shows the performance of the UMS index series, and we can see how over the past month, the currency’s depreciation has helped in the performance of these indices, and in some cases it has evened out YTD losses.

Common risk measures in equities include the volatility of price return and beta measuring price sensitivity to market.  However, in fixed income, volatility measures for bonds are not as straightforward as equities.  First, it can be challenging to obtain reliable daily prices for bonds that do not trade every day.  Second, using the simple measure of price return volatility to construct a low volatility bond portfolio could introduce unintended bias.  For example, given that the price return of a bond is determined by the bond’s duration and yield change, a bond portfolio constructed using the volatility measure of standard deviation of price return could be biased toward bonds with short duration.

In the construction of the S&P U.S. High Yield Low Volatility Corporate Bond Index, an individual bond’s credit risk in a portfolio context is measured by its marginal contribution to risk (MCR), calculated as the product of its spread duration and the difference between the bond’s option adjusted spread (OAS) and the spread-duration-adjusted portfolio average OAS (see Equation 1).  This definition allows for measuring the incremental contribution of each bond to the portfolio credit risk within the framework of duration times spread (DTS).

DTS is an industry-accepted measure of credit risk for corporate bonds, and is calculated by multiplying spread duration and OAS (see Equation 2).  Similar to spread duration capturing bond price sensitivity to spread change, DTS measures bond price sensitivity to the percentage change of OAS (Equation 3).  Ben Dor, Dynkin, Hyman, Houweling, Leeuwen, and Penninga (2007) demonstrate that spread changes are proportional to the level of spreads, i.e., the volatility of percentage spread change is much more stable than absolute spread volatility, and therefore they propose that the better measure of exposure to credit risk is not the contribution to spread duration, but the contribution to DTS.

MCR borrows the concept of DTS by multiplying spread duration by the difference between bond OAS and portfolio average OAS, instead of OAS directly.  By doing so, bonds with low MCR will include those with long spread duration and below average OAS, as well as those with short spread duration and above average OAS.  By selecting bonds with low MCR, the low volatility index keeps more credit exposure (long spread duration) for high-quality bonds (low OAS) and less credit exposure (short spread duration) for low-quality bonds (high OAS).  Overall, this reduces the spread duration mismatch between the low volatility index and the underlying universe.  In the case of stressed bonds with extremely short spread duration, ranking MCR instead of DTS makes it less likely to rank stressed bonds in the lower end, and therefore reduces the likelihood of classifying stressed bonds as low volatility.

As this week’s award of the Nobel Prize in Economics to Richard Thaler confirmed, the existence of behavioral biases in finance is no longer a controversial theory.   People often prefer a small chance of a large gain to a near-certain chance of a small gain, even if the expected return from the latter is higher.  Anyone who doubts this should reflect on the ubiquitous profitability of lotteries; governments have found them a reliable source of revenue, despite the fact that the expected value of buying a lottery ticket is negative.

Analogously, we sometimes attribute the so-called Low Volatility Anomaly to behavioral causes; risk-seeking investors prefer to buy exciting stocks for their perceived upside potential, while investors in lower-volatility stocks reap the reward of risk-seekers’ undue enthusiasm; figuratively, they are selling lottery tickets.

Low Volatility equity strategies have generated their long-term outperformance in part  by mitigating losses in down markets; the price of this loss mitigation is that low vol strategies underperformed in rising markets. This is illustrated in Exhibit 1 for the S&P 500 Low Volatility Index:

Participating in only three quarters of gains can be frustrating for some investors; one way to limit the risk of lagging in bull markets is to combine Low Volatility with different equity factors.    Another approach is to look beyond equities for complementary exposures.  Low volatility investors are not the market’s only behavioral economists.  Volatility-linked investments (such as VIX futures) can also display lottery-like characteristics, with the potential for large returns in a crisis if VIX spikes, and persistent losses otherwise.   Thus, selling VIX futures is another way in which some investors can profit from the lottery-seeking behavior of others.

The S&P 500 VIX Short Term Futures Inverse Daily Index measures the performance of continuously holding and rolling a short position in near-dated VIX futures.  Although it has an intimidatingly long name (we shall use “Short VIX” to refer to it), it has inviting characteristics as a potential diversifier for Low Volatility.  While the same behavioral dynamics may underlie the historically impressive returns both of buying lower-volatility stocks and selling VIX futures, the market dynamics affecting their performance are very different.  Exhibit 2 shows that in months when Low Volatility underperformed the S&P 500, on average Short VIX outperformed.   In months when Low Volatility outperformed, on average Short VIX underperformed.

The Short VIX strategy has a much higher level of absolute risk: an equal combination of Low Vol and Short VIX will be dominated by the latter.  (And the higher volatility of the Short VIX strategy also means that rebalancing frequently might be necessary to keep weights in proportion.)  With those characteristics in mind, Exhibit 3 shows the performance of a hypothetical portfolio (“MIX”), comprising a small position in Short VIX (7.5% weighting), with the remainder in Low Volatility.  The total returns of the S&P 500, and the S&P 500 Low Volatility Index, are shown for purposes of comparison.

While the long-term outperformance of the MIX portfolio is unsurprising (both Short VIX and Low Volatility have outperformed), the key feature of MIX is its upside/downside capture ratio, shown in Exhibit 4:

On a hypothetical basis, the MIX portfolio achieved roughly 100% upside capture (i.e. it did not lag in bull markets), while retaining a considerable degree of protection (77% downside capture).  The MIX portfolio also demonstrated favourable performance statistics over longer periods.  Over all possible rolling 12-month periods during our study, Low Volatility outperformed around 59% of the time; this increases to 72% for the MIX portfolio (Exhibit 5).

The last year reminds us that it can be difficult to remain invested in lower volatility stocks during strong bull markets. The potential addition of a small, rebalanced position in a strategy such as Short VIX could act to diminish the risk of underperformance in rising markets, and provides a potentially coherent way to capture two complementary sources of behavioral outperformance.