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Performance Trickery

The S&P Risk Parity Indices: Methodology

Other Strategies for Fixed Income in Brazil

The S&P Risk Parity Indices: Return Contribution and Leverage

The S&P BSE 500: How Has it Performed?

Performance Trickery

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Craig Lazzara

Former Managing Director, Index Investment Strategy

S&P Dow Jones Indices

Suppose you, as a hypothetical financial advisor, encounter a hypothetical marketer who presents the following hypothetical performance data:

Last Year

Trailing 3 Years Trailing 5 Years

Trailing 10 Years

Portfolio

25.0%

11.9% 16.0%

8.8%

Benchmark

21.8%

11.4% 15.8%

8.5%

Not only did the portfolio beat its benchmark handily in 2017, says our marketer, but it has outperformed consistently over the past decade.  As evidence of this consistency, notice that the portfolio has generated positive value added for the last 3 years, last 5 years, and last 10 years.

Or has it?

Let’s peel the onion a bit.  Here’s the performance of the same hypothetical portfolio for every year in the last 10:

Year Portfolio Benchmark

Value Added

2008

-36.0% -37.0%

1.0%

2009

26.0% 26.5% -0.5%

2010

15.0% 15.1%

-0.1%

2011 2.0% 2.1%

-0.1%

2012

16.5% 16.0%

0.5%

2013

32.0% 32.4%

-0.4%

2014

13.5% 13.7%

-0.2%

2015

1.0% 1.4%

-0.4%

2016

11.0% 12.0%

-1.0%

2017

25.0% 21.8%

3.2%

The portfolio’s value added has been reasonably consistent – it’s been consistently negative, having outperformed in only three years of the past ten.

What’s happening here is that a generally indifferent manager had a really good year in 2017.  The value added in that year compensated for a long history of mediocrity.  Our hypothetical marketer was clever to present his record through a lens that always included 2017.  His numbers were correct, but they were arguably misleading.

There’s a simple lesson in this simple example: If someone shows you trailing performance data, disaggregate.  Look at year-by-year numbers, not cumulative periods ending with the present.  A truly consistent active manager will welcome the scrutiny.

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

The S&P Risk Parity Indices: Methodology

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Berlinda Liu

Former Director, Multi-Asset Indices

S&P Dow Jones Indices

In earlier posts, we analyzed the historical performance, risk contribution versus capital allocation, and return attribution and leverage of the S&P Risk Parity Indices. The results demonstrate that this indices in this series could potentially serve as benchmarks to measure the performance of active risk parity strategies. In this post, we will dig deeper into the methodology and walk through our rationale behind the index rules.

Differences in risk parity strategies arise from the asset classes (and instruments) chosen in the strategy, the risk measurement used, and the handling of the assets’ risk contribution to the portfolio. To build a transparent risk parity benchmark, we use a bottom-up approach that employs long-term realized volatility to allocate across asset classes.

  • Underlying Asset Classes: The underlying asset classes are equity, fixed income, and commodities, as tracked by futures contracts.
  • Liquidity: For all futures contracts used, each has a minimum annual total dollar value traded of USD 5 billion to ensure replicability and tradability.
  • Risk Measurement: We use long-term realized volatility to measure risk. The look-back window has a minimum of a five-year history at the beginning of our back-tested period and is capped at 15 years as we accumulate more data. We use realized volatility rather than forecast volatility to avoid dependency on volatility forecasting models.
  • Weighting Mechanism: We target an equal amount of risk contribution from each asset class to the overall portfolio volatility. In order to do this, we calculate the position weight simply as the pre-defined target volatility divided by the long-term realized volatility for each asset class. Due to correlation among asset classes, the realized volatility of the risk parity portfolio would usually be lower than the target volatility. We then apply a leverage factor to achieve the target volatility. Within each asset class, futures are combined using the same approach to ensure equal risk contribution from futures to the asset class they belong to.
  • Rebalancing Frequency: The indices calculate target weights at month-end and apply them on the second trading day of the next month.

Here is a hypothetical example that illustrates the index construction process of the S&P Risk Parity Index – 10% Target Volatility (TV). In particular, we show how the weight of the S&P 500® futures is determined.

Hypothetical Weighting of the S&P Risk Parity – 10% TV

Suppose the long-term realized volatility of the S&P 500 futures contract is 15%. To reach the target volatility of 10%, we need to allocate 10%/15% = 67% to it and the rest to cash.

Next, we calculate the long-term realized volatility of the equity asset class using weights calculated in step 1. Since there are three futures in the equity asset class, we need to divide their weights by three to construct the equity portfolio.

Suppose the realized volatility of equities is 9%. To reach the target volatility of 10%, we need to apply a multiplier of 10%/9% = 111% to the three constituent futures contract within the asset class. As a result, the weight of the S&P 500 futures in the equity asset class is set to 67% * 1/3 * 111% = 25%.

Finally, we calculate long-term realized volatility of the portfolio using weights calculated in step 2. Since there are three asset classes in the equity asset class, we need to divide their weights by three to construct the multi-asset portfolio.

Let’s say the portfolio’s realized volatility turns out to be 8%. To reach the target volatility of 10%, we need to apply a multiplier of 10% / 8% = 125% to the 26 constituent futures contract. As a result, the weight of the S&P 500 futures is finalized as 25% * 1/3 * 125% = 10%.

The S&P Risk Parity Indices are constructed in a rules-based, transparent manner using tradable, liquid instruments to facilitate implementation. As we have seen in other parts of the blog series, the indices track the composite performance of active risk parity funds much closer than a traditional 60/40 equity/bond portfolio.

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

Other Strategies for Fixed Income in Brazil

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Jaime Merino

Former Director, Asset Owners Channel

S&P Dow Jones Indices

Over the past 10 years, the highest overnight reference rate from the Brazilian Central Bank has been 14.25% from July 2015 through September 2016. In an attempt to hold back the depreciation of the Brazilian real and to keep inflation within the target range, the reference rate dropped 775 bps to 6.50% at the end of August 2018, which is a record low. In addition, of the USD 1.8 trillion in outstanding debt, almost 60% are sovereign bonds. Because of these factors, a new surge of income has been necessary over the past couple of years.

One way to get exposure to fixed income other than typical bonds are derivatives, and S&P Dow Jones Indices, in partnership with B3, has created the S&P/BM&F One-Day Interbank Deposit 3Y Futures Index. The index is designed to measure the performance of a hypothetical portfolio holding a three-year One-Day Interbank Deposit (DI) Futures Contract. The DI contract is on the Brazilian one-day interbank rate, which is used by Brazilian banks to lend and borrow from each other. The contract provides a way to hedge for or speculate on short-term Brazilian interest rates.

The index offers a benchmark for financial institutions to measure the return on their holdings and can serve as the base of an investment vehicle, as the index is easy to replicate and was created with potential tax benefits in mind. It is also calculated in U.S. dollars, which makes it accessible to investors outside of Brazil. Exhibit 1 shows the performance of the indices calculated as excess return and total return.

For the first time, this index provides the opportunity for a local fixed income investment vehicle in the Brazilian market that would provide diversification and could be used to gain core fixed income exposure or to hedge current positions. Exhibit 2 shows the comparison of annual returns with other local indices. In the past 10 years, none of the local indices outperformed in particular, but kept steady. However, when comparing the risk/return profiles, the S&P/BM&F One-Day Interbank Deposit 3Y Futures Index significantly outperformed, meaning that it could help in the reduction of risk in a portfolio (see Exhibit 3).

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

The S&P Risk Parity Indices: Return Contribution and Leverage

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Berlinda Liu

Former Director, Multi-Asset Indices

S&P Dow Jones Indices

My earlier blog showed that equal risk allocation is different from equal capital allocation. The S&P Risk Parity Indices had roughly equal risk contribution from all three asset classes, while about 60% of the capital was allocated to fixed income.

The historical performance of each asset class also showed that equal risk allocation did not lead to equal return contribution (see Exhibit 1). Not surprisingly, fixed income had the highest return to the overall portfolio over the full period studied, as this low risk asset class has significant overweight in risk parity strategies.

The return decomposition of the S&P Risk Parity Index – 10% Target Volatility (TV) showed that the return contribution by asset classes varied significantly from year to year due to changes in the performance of individual asset classes and the correlation among them, affecting the overall portfolio performance. In 2008, equity and commodities experienced market drawdowns, and only the fixed income apportion had a positive return. As a result, the overall portfolio posted a loss.

Another key feature of risk parity strategies is leverage. Risk parity strategies tend to allocate heavily to less volatile asset classes, and managers usually use leverage on low risk asset classes to achieve an overall return that is similar to a market portfolio. The combination of equal risk contribution from multiple asset classes and leverage help a risk parity portfolio to meet the challenges of achieving market returns and reducing the risk of a multi-asset portfolio.

We can see this demonstrated by the S&P Risk Parity Index – 10% TV. For example, its leverage ranged between 1.32 and 2.24 in our back test (see Exhibit 2). Note that leverage is created so that the overall portfolio volatility matches the target volatility each month. As such, leverage rose in low volatility markets and dropped in high volatility markets. On average, the index had a leverage of 168% or 1.68.

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

The S&P BSE 500: How Has it Performed?

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Mahavir Kaswa

Former Associate Director, Product Management

S&P BSE Indices

As highlighted in a prior post, “Getting to Know the S&P BSE 500,” the S&P BSE 500 is considered a proxy for India’s listed equity market, as it covers more than 88% of India’s listed equity universe in terms of total market capitalization. The index also offers diversified exposure to all key sectors of India’s economy and all size segments. In this post, we will review the historical performance of the S&P BSE 500.

As shown in Exhibit 1, from Aug. 1, 2006, to April 30, 2018, the S&P BSE 500 posted an annualized total return of 13.4%, outperforming the S&P BSE 100 and S&P BSE 250 SmallCap Index, while lagging the S&P BSE 150 MidCap Index. The S&P BSE 500 exhibited volatility that was close to the S&P BSE 100’s volatility—not surprising given that S&P BSE 100 constituents account for approximately 79% of the total index weight (see Exhibit 1).

Exhibit 2 shows rolling absolute returns for three-year horizons (750 trading days). All indices compared here performed similarly for most of the period, showing relatively higher correlations with each other. Historically, the S&P BSE 500 and S&P BSE 100 exhibited relatively lower probabilities of negative returns over the three-year investment horizon.

As illustrated in Exhibit 3, out of the total 2,192 trading days observed from Aug 1, 2006, to April 30, 2018, the S&P BSE 500 noted only 113 trading days with returns less than 0%. The relatively more volatile S&P BSE SmallCap 250 Index noted the greatest (452) number of days with negative returns, while the S&P BSE 100, which represents large caps, noted the fewest (52) number of days with negative returns.

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