Investment Themes

Sign up to receive Indexology® Blog email updates

In This List

VIX® Dropped Below S&P 500® Realized Volatility

Rotating Between Growth and Value: The S&P 500® Growth Value Rotator Index

The Calm That Was

Size Matters for Active Large-Cap Fund Performance

Potential Applications of the Low Volatility High Dividend Concept in Brazil

VIX® Dropped Below S&P 500® Realized Volatility

Contributor Image
Berlinda Liu

Director, Global Research & Design

S&P Dow Jones Indices

two

While everyone has been concerned about the inverted yield curve, the CBOE Volatility Index® (VIX) has been under the 21-trading-day realized volatility of the S&P 500 since Aug. 16, 2019. Since volatility traders care not only about what is expected but also what actually transpired, the spread between implied volatility and realized volatility is one of the most important gauges for them to keep an eye on.

Historically, implied volatility tends to stay above realized volatility due to the skewed distribution of stock returns. When implied falls below realized, it usually suggests option premiums are relatively cheap, therefore favoring option buyers.

Implied volatility represents the current market price for volatility, or the fair value of volatility based on the market’s expectation for movement over a defined future period of time. VIX is arguably the most-followed gauge of the U.S. equities market implied volatility in the next 30 calendar days.

Realized volatility, on the other hand, is the actual movement that occurred in a given underlying over a defined past period. For VIX, that underlying is the S&P 500. Since the S&P 500 trades only when the market is open, the convention is to compare VIX with the realized volatility from the previous 21 trading days, approximately one calendar month.

Calendar-year averages since 2000 show that it is “normal” when VIX is above the realized volatility of the broad equity market (see Exhibit 1). The spread, calculated as VIX minus the 21-trading-day realized volatility of the S&P 500, is usually around 3-4 points. It tended to narrow during periods of market turbulence (e.g., in 2000 and 2008); 2008 was the only year that average VIX readings, which were higher than usual, fell short of the realized volatility. On the contrary, while the 2017 VIX level was lower than usual, the relationship between implied and realized volatility remained fairly “normal.” 

On a day-to-day basis, if we marked all the days as 1 when VIX was higher than the realized volatility and as -1 when VIX was lower than the realized volatility, we can visualize the frequency of positive and negative implied/realized volatility spreads over the past 20 years (see Exhibit 2). Since July 1, 1999, negative spreads occurred on 796 days out of 5,070 (15.7% of the time). Prolonged periods of negative spreads tended to occur in turbulent markets such as those in 2000 and 2008. The most recent stretch of negative spreads occurred from Q4 2018 to early 2019 during the market sell-off.

Why is implied volatility normally higher than realized? From a behavioral finance perspective, this is an indication of risk aversion—investors are willing to pay a premium to buy protection against risk. From an option pricing perspective, it is because stocks and stock indices do not follow the log-normal distribution assumption of Black-Scholes. The empirical distribution of stock returns has a negative skew and hence reflects larger losses than a normal or log-normal model using the ex-post mean and standard deviation would predict. Implied volatility takes into account large but rare events, while realized volatility will only include such events if they have occurred in the look-back calculation period. Since low-probability events are rare by definition, realized volatility tends to understate the potential for large losses most of the time. However, in a distressed market, realized volatility tends to overstate the risk of large losses when these sudden moves indeed occurred in the calculation window.

Nevertheless, in the relationship between implied and realized volatility, realized volatility serves as the baseline, while implied volatility defines the relative values of option premiums. The negative spread between VIX and realized volatility reflects overall complacency in the market.

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

Rotating Between Growth and Value: The S&P 500® Growth Value Rotator Index

Contributor Image
Qing Li

Director, Global Research & Design

S&P Dow Jones Indices

two

Growth and value are two investment styles based on fundamental analysis. A growth company typically has promising earnings potential and tends to invest more in future growth rather than dividend payouts, while a value company tends to be more established, with stable growth rates and dividend distributions.

While most market participants are familiar with single-style investment strategies, rotating between growth and value could help enhance return potential. The recently launched S&P 500 Growth Value Rotator Index uses a simple momentum rotation signal to switch between growth and value strategies, and it outperformed single-style benchmarks and the broad equity market with lower risk (see Exhibit 1).

Studies[1], [2] have not shown any strong evidence that one style consistently outperforms the other. If we use the S&P 500 Growth and the S&P 500 Value to represent growth and value stocks, we can see that growth stocks outperformed value stocks 51% of the time, while value stocks produced better returns 49% of the time, both on a total return basis, from January 1995 to July 2019.

The goal of the S&P 500 Growth Value Rotator Index is to take advantage of the two styles. It shifts between growth and value strategies based on the momentum rotational signal, calculated as the 12-month return of the growth or value indices. The strategy is constructed as an index of indices, taking the total returns of the S&P 500 Growth Index and S&P 500 Value as the underlying benchmarks. At the end of each month, if the 12-month return of the S&P 500 Growth is greater than the 12-month return of the S&P 500 Value, the index allocates to the S&P 500 Growth, and vice versa. The existing index selection remains unchanged if the one-year returns of growth stocks and value stocks are the same.

The S&P 500 Growth Value Rotator Index delivered significant outperformance from February 1995 to July 2019, producing an average monthly excess return of 0.15% over the overall stock market, as measured by the S&P 500. In addition, the index outperformed the S&P 500 Growth and the S&P 500 Value by 1.38% and 2.75% on an annualized basis, respectively. The reduced volatility enabled the S&P 500 Growth Value Rotator Index to achieve the highest risk-adjusted return among the individual style indices and the S&P 500 (see Exhibit 1).

The outperformance of the S&P 500 Growth Value Rotator Index was most evident in long-term horizons. In the 10-, 15-, and 20-year periods ending in July 2019, the strategy demonstrated consistent outperformance over the single-style strategies and the broad market, on an absolute and risk-adjusted return basis (see Exhibit 2). Over short-term horizons (the three- and five-year periods), the S&P 500 Growth Value Rotator Index notably outperformed the S&P 500 Value and the S&P 500, while its return slightly underperformed the S&P 500 Growth. This is not surprising since the market has been in an expansion stage in recent years. Furthermore, the S&P 500 Growth Value Rotator Index exhibited the smallest maximum drawdown of 49.73%, compared with 56.82%, 53.40%, and 50.95% for the S&P 500 Value, the S&P 500 Growth, and the S&P 500, respectively.

[1] N. Beneda [2003] Growth Stocks Outperform Value Stocks Over the Long Term

[2] A. Ang [2018] Value Investing: The Long-term Appeal of the Underdog; Grow is not the Opposite of Value

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

The Calm That Was

Contributor Image
Fei Mei Chan

Director, Index Investment Strategy

S&P Dow Jones Indices

two

Through the end of July, equities had netted a nice gain for 2019 (though the picture looks a lot different so far in August). Unusually, the S&P 500 Low Volatility Index® outperformed in an environment when it has typically lagged its benchmark. (The S&P 500 gained 20.2%, while the low volatility index was up 20.8%, thru July 2019.)

Similar to its last quarterly rebalance, turnover in the low volatility index was limited, with changes taking place following the market close on August 16, 2019. Sector allocations are strikingly similar to the previous rebalance, with Financials, Technology and Utilities adjusting slightly higher while Consumer Discretionary, Health Care and Real Estate scaled back marginally.

The Latest Rebalance for the S&P 500 Low Volatility Index Yielded Minimal Changes

Trailing one-year volatility for S&P 500 sectors, a gauge we use sometimes use to gain insight, barely budged in the last three months. This is consistent across all 10 sectors. It’s therefore not surprising that turnover activity over the last two rebalances is at the lowest annualized level on record.

252-Day Volatility Changed Little Across All S&P 500 Sectors Compared to Three Months Ago

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

Size Matters for Active Large-Cap Fund Performance

Contributor Image
Berlinda Liu

Director, Global Research & Design

S&P Dow Jones Indices

two

Over the 15-year period ending March 31, 2019, the biggest 25% of active large-cap equity funds managed about 90% of all the assets under management (AUM) held in active large-cap equity funds. This may suggest that investors’ fund selections skew toward larger funds. But is bigger always better? This topic has been widely debated: although larger funds may be able to hire more skilled fund managers and, in return, managers’ successful track records may attract more assets, one could also argue that smaller funds may face less liquidity constraints in security selection and can “move the needle” with relatively small investments.

In this blog, we explore the effect of size—as measured by AUM—on active large-cap equity funds’ performance. Our study shows that, in the large-cap equity fund category, larger funds tended to take more risk and generate higher returns than smaller ones. However, their main advantage lies in higher survival rates over the long run, which contributed to their lower percentage of underperformance relative to the benchmark.

Over the 1-, 5-, 10-, and 15-year horizons, we first rank all long-only active large-cap equity funds by their size at the beginning of the period and divide them into quartiles, with the first quartile being the largest and the fourth being the smallest. We then compare their returns, volatilities, survival rates, and ability to outperform the S&P 500®. To eliminate the confounding factor of equity capitalization, we limit the universe to large-cap equity funds only. Fund returns are on a net-of-fee basis.

Larger funds were more likely to survive the market cycle than their smaller peers, especially over longer horizons (see Exhibit 1). Among the smallest 25% of funds (fourth quartile) that existed at the beginning of the 15-year study period, only 1 out of 5 survived the entire period compared with a survival rate of over 60% for the largest funds (first quartile) over the same period.

Larger funds also tended to fare better against the broad equity market (see Exhibit 2). For example, in the one-year period ending in March 2019, around a third of first quartile funds beat the S&P 500 compared with only 25% for fourth quartile funds. This difference became more pronounced over longer horizons; the low survival rate among smaller funds helps to explain this result given we assume dead funds underperformed the benchmark. In fact, if we account only for funds that have survived the 15-year period, about 73% of funds in the first quartile and fourth quartile underperformed the benchmark (see Exhibit 3).

We next calculated annualized returns and volatilities of all the surviving funds (see Exhibit 4). On average, larger funds showed higher returns and took higher risk than the smaller funds in the 1-, 5-, and 15-year periods. However, this tendency gradually diminished over longer time horizons: the largest funds generated 30 bps of extra annualized returns compared with the other three groups over the 15-year horizon. The 10-year returns and volatilities indicated that large funds were more conservative than their peers during the 2008 financial crisis and its subsequent recovering period.

The third quartile funds (i.e., the second smallest fund group) showed comparable and sometimes even higher returns than the largest ones. This may help to explain why third quartile funds sometimes performed better against the S&P 500 than the other three groups (see Exhibits 2 and 3). Interestingly, these funds did not take extra risk compared with the largest ones.

Experience the active vs. passive debate on a global scale on Indexology®.

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

Potential Applications of the Low Volatility High Dividend Concept in Brazil

Contributor Image
Maria Sanchez

Associate Director, Global Research & Design

S&P Dow Jones Indices

two

Historically, the percentage of dividend payers in Brazil has ranged between 71% and 87%,[1] making it a propitious environment for implementing dividend-focused strategies. The highest-yielding stocks in high-yield strategies often come with greater portfolio volatility,[2] and Brazil is no exception. This blog explores the rationale behind the implementation of a low volatility high dividend strategy in Brazil and its potential benefit.

Low volatility high dividend strategies aim to provide yield at a reasonable risk level. To review the characteristics of high dividend yield stocks in Brazil, we separated dividend payers from the S&P Brazil BMI universe into hypothetical quintiles based on yield. Securities in each quintile are equal weighted and held for 12 months. Our results showed that the securities in Quintile 1 (the highest dividend yielding stocks) had the highest average 12-month holding period returns (see Exhibit 1).

Then, we went through the same exercise but based the quintiles on their 12-month trailing volatility. As shown in Exhibit 2, securities with lower volatility (Quintiles 1 and 2) had higher risk-adjusted returns (0.56 and 0.55, respectively) while securities in the higher volatile buckets (Quintiles 3, 4, and 5) had much lower risk-adjusted returns (0.28, 0.34, and 0.24, respectively).

Our approach to combine high yield with low volatility consisted of two steps. First, we selected the top 50% of stocks with the highest dividend yield; second, from that subset, we selected the top 40% with the lowest volatility stocks.

To demonstrate the possible benefits of our approach, we created three hypothetical portfolios based on the dividend payers of the S&P Brazil BMI and measured their historical returns from Dec. 31, 2007, to June 28, 2019.

  1. High Yield portfolio: 50% of stocks with the highest dividend yield of the dividend payers of S&P Brazil BMI.
  2. Low Volatility High Yield portfolio: 40% of the lowest volatility stocks selected from the High Yield portfolio.
  3. High Volatility High Yield portfolio: 60% of the highest volatility stocks from the High Yield portfolio.

All portfolios were rebalanced in June and December, and all portfolio members were equally weighted.

The results show that over the mid- and long-term periods, the Low Volatility High Yield portfolio outperformed the High Yield and High Volatility High Yield portfolios with less risk, delivering better risk-adjusted returns (see Exhibit 3).

To review how these hypothetical portfolios performed in the most significant down markets, we looked at the three largest drawdowns of the S&P Brazil BMI since Dec. 28, 2007. In all the drawdown periods, the Low Volatility High Yield portfolio outperformed the benchmark and High Yield and High Volatility High Yield portfolios (see Exhibit 4).

Combining low volatility and high dividend yield strategies by using a two-step screening process when constructing a high dividend index could potentially provide better risk-adjusted returns than a high yield strategy, capturing the benefits of high dividend and low volatility strategies.

[1]   Source: S&P Dow Jones Indices LLC. Data for S&P Brazil BMI from Dec. 31, 2007, to June 28, 2019.

[2]   Luk, Priscila and Qu, Xiaoya, “The Beauty of Simplicity: The S&P 500® Low Volatility High Dividend Index,” 2019, S&P Dow Jones Indices LLC.

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