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Strong Returns Over 32+ Years for BXMD Index That Writes OTM SPX Options

“Counter-cyclical” adjustment factor resumed to anchor Chinese currency

Relative Performance Impacts From the Introduction of Communication Services

Getting to Know the S&P BSE 500

The S&P Risk Parity Indices: Risk Contribution Versus Capital Allocation

Strong Returns Over 32+ Years for BXMD Index That Writes OTM SPX Options

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Matt Moran

Head of Global Benchmark Indexes Advancement

Cboe Global Markets


In recent years at many investment conferences I heard that many public pension plans face underfunding challenges, and they are very interested in investments with strong returns and moderate risk. While past performance is not a guarantee of future results, the Cboe S&P 500 30-Delta BuyWrite Index (BXMDSM) has shown higher returns and lower volatility than the S&P 500®, MSCI EAFE®, and S&P GSCI indexes in the period from its inception in mid-1986 through July 2018.


Note in the line chart below that since mid-1986 the BXMD index (which writes out-of-the money (OTM) SPX options) (a) rose 2527%, and (b) generally had bigger upside moves during bull markets than the Cboe S&P 500 BuyWrite Index (BXMSM), (which writes at-the-money (ATM) SPX options).


The BXMD Index is designed to track the performance of a hypothetical covered call strategy that holds a long position indexed to the S&P 500 Index and sells a monthly out-of-the-money (OTM) S&P 500 Index (SPXSM) call option. The call option written is the strike nearest to the 30-Delta SPX call option at 10:00 a.m. CT on the Roll Date.


As shown in the two bar charts below, the BXMD Index had the highest annualized returns of the nine indexes shown, and the BXMD also had lower volatility than the S&P 500 and S&P GSCI indexes.


The histogram with the S&P 500 and BXMD indexes shows that the S&P 500 had 13 months with declines of worse than 8 percent, while the BXMD Index had 9 such months. Certain index options strategies can be used to help manage left tail risk.


In the Returns and Volatility chart, the BXMD Index had the highest returns of all 11 indexes, and had lower volatility than the stock and commodity indexes.


In the table below with metrics for 12 benchmark indexes:

HIGHEST RETURNS. The BXMD Index had the highest annualized returns;

HIGHER BETA. When compared to the market (the S&P 500), the BXMD Index had a beta of 0.82 and an r-squared of 90.55%; both of these numbers are higher than for most of the other option-based benchmark indexes. A strategy that writes OTM index options has the potential to more closely track its related stock index when compared to a strategy that writes ATM index options. The OTM option writing usually takes in lower premiums than the ATM index option writing strategy, but the OTM strategy can participate in the upside moves in bull markets. The BXMD Index is not designed for diversification and portfolio risk reduction goals.

HIGH RISK-ADJUSTED RETURNS. In the table below, the three indexes with the highest Sharpe Ratios were the Cboe S&P 500 Putwrite Index (PUTSM) (0.687); Cboe S&P 500 Covered Combo Index (CMBOSM) (0.591) and the BXMD Index (0.586). The Sharpe Ratio is one of the most popular metrics for risk-adjusted returns, but a key caveat in use of the Sharpe Ratio is that it works best when comparing investments with normal distributions of returns, but most of the indexes in the table below have non-normal distributions of returns with negative skewness. The skewness over the period of 32+ years was negative 2.14 for the PUT Index, negative 1.11 for the BXMD Index, and negative 0.81 for the S&P 500 Index.


An inquisitive investor might ask – how could the BXMD have higher returns, lower volatility, and higher risk-adjusted returns than several key “traditional” indexes over several decades? One possible explanation for the strong relative performance of the BXMD Index is the volatility risk premium – in recent decades SPX options generally have been richly priced. Exhibit 8 of the paper by Wilshire at shows that S&P 500 implied volatility usually has been higher than subsequent realized volatility, and this has facilitated higher risk-adjusted returns for option-selling indexes such as the BXMD and PUT indexes.

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

“Counter-cyclical” adjustment factor resumed to anchor Chinese currency

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Jack Jiang

Senior ETF Specialist, Index and Quantitative Investment

ICBC Credit Suisse Asset Management (International) Co., Ltd.


China resumed CNY fixing “counter-cyclical” adjustment factor

The People’s Bank of China (PBOC) announced on August 24 that China’s CNY fixing reporting banks have resumed the counter-cyclical adjustment (CCA) factor in the CNY official midpoint this month. This is the second move of Chinese authorities after the adoption of a 20% reserve requirement of FX forward positions on August 3. According to the PBOC, the moves are aimed at mitigating the RMB depreciation pressure arising from the procyclical sentiment caused by the strong USD and ongoing trade frictions.

 What is the counter-cyclical factor?

The counter-cyclical adjustment (CCA) factor was first introduced in May 2017 as a third factor on top of the basket of trade-weighted currency indices and closing spot level at 4:30 pm of the previous trading day when settling the official midpoint. The intention of this additional factor was to ward off the one-way bets on the yuan and the subsequent capital outflows.

The first introduction of the CCA in May 2017, when the yuan was also weak at around 6.9 against the U.S. dollar, stemmed the depreciation trend and was followed by sustained strengthening in the yuan against a basket of currencies. The S&P China 500 turned upward as well (see Figure 1). This factor was suspended in January 2018 as the depreciation pressures dissipated.

A signal to support the yuan

The announcement was seen as a signal from the PBOC that it is not comfortable with further depreciation in the yuan, as it could trigger capital outflows.

The yuan hovered at a 2.5 week high against the U.S. dollar on Monday, arresting the slump from the middle of June that has rattled global markets. (The S&P China 500 followed the trend again as indicated in Figure 1).

Some market players go with the hypothesis of a “7-3” threshold for stability—that is, not breaching 7 (or around 6.9) for the U.S. dollar vs. the yuan and FX reserves not falling below USD 3 trillion for Chinese regulators.

Nevertheless, in the long term, the currency rate of the world’s second-largest economy will rest with the expected return and risk premium on the investment.


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

Relative Performance Impacts From the Introduction of Communication Services

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Philip Murphy

Managing Director, Global Head of Index Governance

S&P Dow Jones Indices


S&P Dow Jones Indices will reshuffle stocks in September to reflect the revised structure of the Global Industry Classification Standard (GICS®) in its indices. The move will see telecommunication services replaced by a new sector called communication services. Its constituents will transfer from telecommunication, information technology (IT), and consumer discretionary.

Exhibit 1 shows affected S&P 500® sector weights as of July 10, 2018. Pro forma sector weights represent the index on July 10, 2018, as if communication services was in effect at that time. After September 2018, S&P 500 sectors should be more evenly distributed by index weight than they now are. Pro forma IT would lose almost 6% of its index weight, consumer discretionary almost 3%, and communication services would gain the difference.

The performance of headline cap-weighted benchmarks like the S&P 500 will not be affected by the GICS change. However, changes will occur in performance of affected sectors, stock contribution to sector returns, sector contribution to benchmark returns, and traditional performance attribution. In turn, assessments of active managers’ value creation/destruction may be affected.

To show a hypothetical impact of the GICS change on stock contributions to sector returns, Exhibit 2 provides data from the S&P 500 Information Technology sector for the one-year period from July 31, 2017 through July 31, 2018. Including reinvestment of dividends, the S&P 500 IT Sector returned 28.48%, Apple (AAPL) stock returned 29.95%, and Microsoft (MSFT) returned 48.76%.

What are the mechanics behind pro forma changes in sector performance and stock contributions? With the departure of names like Alphabet (GOOG; GOOGL), Facebook (FB), and others, the IT sector will lose weight in the S&P 500. However, within the IT sector remaining stocks will be up-weighted to compensate for those departures. All else equal, returns of large stocks like Apple (AAPL) and Microsoft (MSFT) will therefore have even more impact on sector performance. On a pro forma basis, Apple’s and Microsoft’s strong returns for the one-year period ending July 2018 would have combined with their larger weights within IT to help pull up the overall return of the sector to 33.27%—an even higher bar for active technology stock pickers! Of course, circumstances in every period are different and the opposite could have been true.

Structural updates to GICS, such as the addition of the real estate sector several years ago, and the upcoming shift to communication services, are evidence that GICS is a dynamic framework seeking to reflect economic evidence and trends. Stakeholders utilizing sector-based performance contribution and/or attribution analysis should be aware of these changes and the potential effects they can have on their analyses.

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

Getting to Know the S&P BSE 500

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Mahavir Kaswa

Associate Director, Product Management

S&P BSE Indices


Over the past three to four decades, the Indian equity market has witnessed significant growth, on account of increasing foreign capital flows and participation by domestic institutional investors. The expanding depth and breadth has brought a stronger demand from market participants for suitable benchmarks. The S&P BSE SENSEX launched in 1986 and was the first and most popular Indian benchmark, followed by the S&P BSE 100 and S&P BSE 200 in 1989 and 1994, respectively. In 1999, the S&P BSE 500 was launched in response to market demand for broader benchmarks that offer more complete coverage of the Indian equity market.

The S&P BSE 500 is designed to be a broad representation of the Indian equity market, consisting of the 500 leading companies in terms of total market capitalization that are members of the S&P BSE AllCap. The differential voting rights shares class is eligible to be part of the index, which means that at any point in time, the index will include a fixed number of 500 companies, but the number of stocks in the index could be greater than or equal to 500. The index constituents and sectors are weighted in proportion to their float market capitalization. The index is reviewed semiannually in June and December.

In a recently published paper titled “Measuring Indian Equities: The S&P BSE 500,” we saw that as of April 30, 2018, the S&P BSE 500 represented approximately 88% of all BSE-listed companies, with a total market capitalization of INR 1,35,14,943 crores (approximately USD 2 trillion).

The S&P BSE 500 is diversified across sectors, and no individual sector had an excessive overweight in the index in the period studied. As shown in Exhibit 1, the financials sector had the highest weight in the index, with 30%, followed by consumer discretionary at 12.5%. The services sectors, which include financials, information technology, and telecommunications services, contributed nearly 41% to the total index weight. Also, the combined weight of constituents that have individual derivative contracts was 87%, which facilitates risk management of the index.

Similarly, the S&P BSE 500 offers exposure to all size segments. Large-cap stocks accounted for 79% of the total index weight, mid caps 13%, and small caps 8% as of April 30, 2018 (see Exhibit 2).

With coverage of more than 88% of India’s listed equity universe and diversified exposure to all sizes and all key economic sectors of India’s economy, the S&P BSE 500 seeks to provide comprehensive coverage of the Indian equity market.

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

The S&P Risk Parity Indices: Risk Contribution Versus Capital Allocation

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Berlinda Liu

Director, Global Research & Design

S&P Dow Jones Indices


In a prior blog, we showed that the S&P Risk Parity Indices tracked the average performance of active risk parity funds closer than a traditional 60/40 equity/bond portfolio. In this second part of the blog series, we will examine the risk contribution and capital allocation of these indices.

The principles behind risk parity strategies relate to answering a deceptively straightforward question: What is diversification? Traditionally, investors have allocated their capital across multiple asset classes to achieve diversification, such as the 60/40 equity/bond blend. A previous blog by Phillip Brzenk showed that such an approach led to a disproportionate allocation of risk across asset classes, with equities occupying almost all of the portfolio risk. A risk parity strategy, on the other hand, aims for balanced risk contribution from all asset classes. A proper risk parity benchmark should demonstrate roughly equal risk contribution from all asset classes.

Exhibit 1 shows the back-tested historical risk contribution of the S&P Risk Parity Index – 10% Target Volatility. Over the past 14 years, equities, fixed income, and commodities have contributed roughly the same amount of volatility to the portfolio, despite some fluctuations over time. We can see that the risk contribution of the different asset classes varies based on different market environments. This is not surprising, as we use realized volatilities as the risk measure in the index methodology. For example, in 2008, when the equity market was experiencing a bear market, the risk contribution by equities reached an all-time high of 42.79%. After the volatility seen in 2008 was included in the realized volatility calculation, equity risk contribution subsided to 33.99% in 2009, close to the one-third risk attribution expected in a risk parity portfolio. Despite fluctuations in annual risk attributions, the three asset classes contributed almost equally to the portfolio volatilities over time, either measured as mean or median (see Exhibit 1).

As seen in Exhibit 2, the historical capital allocation verifies that equal risk allocation did not lead to equal capital allocation. Fixed income, the least volatile asset class among the three, has the largest capital allocation to ensure its equal risk contribution to the portfolio. During the 14-year back-tested period, about 60% of the capital was allocated to fixed income securities (mean = 60.0%, median = 62.3%). The remaining 40% of the capital was split between equities (mean = 19.8%, median = 18.2%) and commodities (mean = 20.2%, median = 19.7%) almost evenly. The allocations among the three asset classes were fairly stable over time.

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