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In this article, the authors explore six quantitative environmental (E), social (S), and governance (G) strategies to provide insights into best practices for ESG portfolio construction. These strategies offer different approaches to the trade-off between desired ESG attributes and investment performance. They conclude that fully understanding the dynamics of these trade-offs will allow investors to select the strategy that best matches their ethical and financial views.
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Tracking Error Forecasts in a Low-Volatility Environment

ran leshem
Observations
by Ran Leshem
August 1, 2018

Over the past couple of years, the equity markets appeared to have experienced a period of extremely low volatility. The one-year trailing volatility on the S&P 500 reached 6.77% at the end of 2017! In response to the low volatility, our risk models have been forecasting tracking errors that are materially lower than historical levels. This behavior has been consistent across Aperio’s tax-managed, ESG, and factor-tilted portfolios. In this blog post, we will analyze the historical relationship between tracking error and overall volatility. We will also estimate the magnitude of the expected change in tracking error should volatility increase back to historical levels.

rolling 12-month realized volatility of S&P 500

Source: S&P (us.spindices.com/indices/strategy/sp-500-12-month-realized-volatility-index).

To analyze the relationship between tracking error and volatility, we simulated two portfolios: a portfolio tracking the S&P 500 with 200 assets (“Standard–200”) and an Aperio value-tilted portfolio (“Value”) over the period from 1/1/1998 to 3/31/2018. We chose a standard portfolio with 200 names to intentionally introduce tracking error into the strategy. The chart below shows the forecast tracking error of the simulated portfolios as well as the forecast volatility of the benchmark (gray line) over the past 20 years. The dotted lines show the averages over the full period. The forecasts were made using the Barra US long-term risk model, which updates daily and uses historical data exponentially weighted with a half-life of 252 days.

forecast tracking error and volatility (1998-2018)

Sources: Aperio Group, LLC, MSCI Barra (please see text for additional information).

The forecast tracking error for both portfolios in this simulation spiked in periods of high volatility (dot-com bust and the financial crisis) and decreased in periods of low volatility. Tracking error forecasts capture both asset and factor deviation from the benchmark, as well as the volatility of factors and individual assets. The forecasts therefore should increase (decrease) in volatile (stable) regimes. Given the current low-volatility environment, it is not surprising to see forecast tracking error at its lowest levels in the past 20 years. If market volatility rises again, history strongly suggests tracking errors should increase as well.

One way to estimate the magnitude of the potential increase is to look at a ratio of current forecast tracking error as a percentage of the historical average forecast tracking error. The current forecast is approximately 25% below the historical average. If we believe market volatility and forecast tracking error might increase by 25%, the absolute increase for low tracking error portfolios will be small. For example, a 0.36% tracking error portfolio (like the Standard-200) may move up to 0.47%. Using the same process for higher tracking error portfolios like Value would project higher absolute jumps.

forecast tracking error relative to historical average (1998-2018)

Sources: Aperio Group, LLC, MSCI Barra.

What does this mean to clients?

Current tracking error forecasts appear to be materially lower than historical levels. We believe that this may be driven by the low-volatility environment of the past couple of years. If volatility were to return to historical levels, we would expect actual/forecast tracking errors to be higher than current forecasts. Therefore, clients making tracking error decisions on transitions or ESG menu implementations may want to opt for lower tracking error choices if they expect market volatility to return to historical averages. Clients using factor strategies may want to also rely on long-term back-tested tracking errors for expectation management and not solely rely on current forecasts.


Send questions or comments to blog@aperiogroup.com.

This article is provided for informational purposes only. The information contained within this article was carefully compiled from sources Aperio believes to be reliable, and it is accurate to the best of our knowledge and belief. However, Aperio cannot guarantee its accuracy, completeness, and validity, and cannot be held liable for any errors or omissions. All information contained herein should be independently verified and confirmed. Aperio does not accept any liability for any loss or damage whatsoever caused in reliance upon such information. Aperio provides this information with the understanding that it is not engaged in rendering legal, accounting, or tax services. In particular, none of the examples should be considered advice tailored to the needs of any specific investor. Aperio recommends that all investors seek out the services of competent professionals in any of the aforementioned areas. With respect to the description of any investment strategies, simulations, or investment recommendations, Aperio cannot provide any assurances that they will perform as expected and as described in this article. Past performance is not indicative of future results. Every investment program has the potential for loss as well as gain. You cannot invest directly in an index.