Scenario Tester Methodology

The BlackRock Scenario Tester measures the potential impact of discrete market events on a portfolio using a multi-factor statistical risk model. It does this via a three-step process:

  1. Based on the current holdings of the portfolio, determine a portfolio’s exposure to a variety of risk factors in equity, fixed income, alternative and currency asset classes.
  2. Estimate the volatility of these risk factors and their sensitivity (correlation) to the relevant market event using 10 years of history.
  3. The portfolio’s sensitivity to the market event is calculated as the aggregate impact of these factors. This incorporates the portfolios exposure to the factors; the volatility of the factors; the diversification of factors across the portfolio; and the sensitivity (correlation) of the factors to the market event being evaluated.

 What do the results show?

The scenario test results show a statistical estimate of a portfolio or asset class’s reaction to a hypothetical market event. For example, we may calculate that a portfolio could expect to return -5% in the event that the S&P 500 were to fall -15%. It is important to understand that this is just one potential outcome, as a statistical estimate is sensitive to assumptions built into our risk model. More detail on the assumptions and limitations of our risk model are below.

The calculated return is the underlying price movement in reaction to the scenario. There is the option to add yield to the calculated return; this yield is meant to estimate a level of income that the portfolio could receive over a 12 month period. The yield is represented by the weighted average 12 month trailing yield of the portfolio. The 12 month trailing yield is the percentage income the portfolio or asset class returned over the past 12 months through fund distributions, stock dividends, and interest on fixed income instruments. The difference between trailing 12 month yield and 30 Day SEC Yield is that the latter reflects the income earned after deducting the fund's accrued expenses, excluding reimbursements, during the most recent 30-day period. The income distributions an investor may receive in the future may be higher or lower than the yield shown. Past performance does not guarantee future results.

What is a risk factor?

A risk factor is a technical or fundamental characteristic of a security which, statistically, is able to help explain the risk / return behavior of that security.

We believe risk factors should be:

  1. Interpretable: easily understandable and have a strong economic rationale
  2. Explanatory: proven to explain volatility and correlation of returns
  3. Consistent: significant through time across different market regimes
  4. Effective: able to improve forecasts of portfolio risk and attribute portfolio performance

BlackRock’s risk model uses over 2,200 distinct risk factors across equity, fixed income, currencies and alternative investments.

Examples of risk factors include:

  • Equity market risk– the inherent risk of investing in the stock market, which cannot be diversified away with stocks alone.
  • Equity style risk – a collection of factors that describe the style of a stock, such as growth, value, momentum, or size.
  • Interest rate risk – a fixed income risk attributed to changes in market interest rates, the dominant risk in high quality bonds like US Treasury securities.
  • Credit spread risk – the risk of default present in any bond not issued by the US Treasury. The lower the quality of the bond, the more credit spread risk dominates.
  • Many others that are subsets of the categories above, as well as other categories like foreign exchange risk, inflation risk, and alternative risk.

What assumptions are built into the risk model?

The model for this tool uses ten years of historical factor returns, derived using a statistical regression analysis, to measure the volatility of the factors we are stressing. The portfolio’s exposure to each factor is measured through currently observable characteristics of the underlying securities, for example fundamental characteristics such as financial ratios, technical analysis attributes such as price behavior or liquidity, and / or specific attributes of a given security such as yield, geographic domicile and currency exposure. The level of exposure to a factor of a given security corresponds to where the security ranks relative to the distribution of all securities in the universe for the characteristic in question, for example whether the price to book ratio for a company is very high or very low relative to other companies. Historical covariance across the factors is taken into account to measure total estimated volatility of the overall portfolio. Certain elements of the risk model are proprietary to BlackRock.

How accurate are the results?

The scenario test results show a statistical estimate of a portfolio or asset class’s reaction to a hypothetical market event. This is done using a risk factor framework, as described above. The scenario test results are subject to uncertainty, and we can measure this uncertainty using the prediction error. The prediction error describes the range of possible outcomes around the scenario test result where 95% of possible outcomes should occur. For example, we may calculate that a portfolio could expect to return -5% in the event that the S&P 500 were to fall -15%, with a prediction error of 2%. Based on the prediction error of 2%, we would expect that 95% of the time the portfolio would return between -3% and -7% (-5% +/-2%) in the event that the S&P 500 fell 15%.

The prediction error will vary depending on the portfolio or asset class and the scenario. A larger prediction error implies that the test result may be less certain. Prediction errors tend to be larger when the underlying shock is larger; the risk factor model is less able to explain the portfolio risk (for example, in investments with a high proportion of idiosyncratic risk, like certain hedge funds); and/or the risk factors in the scenario are less correlated with the risk factors in the portfolio (for example, evaluating a US equity portfolio in a scenario driven by Japanese interest rates). Conversely, prediction errors tend to be smaller when the underlying shock is smaller, the risk model is a good fit for portfolio risk, and the factors in the scenario are more correlated with the portfolio.

How do we represent funds in the risk model?

Funds are modeled in Aladdin using the best available data, and therefore methodology varies by asset class and fund type. There are three methodologies that are used to model funds.

Holdings: Equity funds are modeled through holdings when they are available. For an equity fund, its holdings provide the key to capturing the undiversified idiosyncratic risk from large single-name positions. Equity idiosyncratic risk is a key contributor to total risk, in addition to exposures to systematic equity market, sector and style factors.

Building Blocks: Fixed Income funds are modeled through building blocks based on the granular fund sector allocation. For a fixed income fund, its exposure to different sectors (government, corporate, securitized, etc.) provides the key to capturing the exposures to interest rate and spread factors that drive its risk. Each sector allocation is translated into representative investable bond portfolios (building blocks) that also accounts for the fund’s strategy (e.g. short, intermediate or long duration). Similarly, Multi-Asset funds are modeled based on the fund’s sector allocations, in order to capture the risk driven by asset allocation decisions. A blend of building blocks representing the different asset class allocations is used to capture the risk. The building blocks are all modeled from holdings with the Aladdin risk models.

Return Regression: Alternative funds are modeled by a return regression approach to capture the persistent market exposures and obtain average exposures to different market factors, hedge fund style factors and idiosyncratic risk over a historic period.

What are the limitations of the analysis?

While the funds modeling process uses the best available data to represent holdings and/or factor exposures, there is no guarantee that it is perfect representation of the fund.

By default, dividend income and yield are not included in the return calculations; an income yield can be added to the returns if the option is selected, but this is an approximation based on past performance. Fees, taxes and transaction costs are not included and would reduce the return of any portfolio.

The analysis provided is illustrative only. Neither BlackRock nor the Aladdin portfolio risk model can predict a portfolio’s risk of loss due to, among other things, changing market conditions or other unanticipated circumstances. The Aladdin portfolio risk model is based purely on assumptions made using available data and any of its predictions are subject to change.

How did we choose the size of the shocks?

The shocks are sized to approximate a one standard deviation move in the underlying market factor. We measure the factor’s annualized standard deviation over the past ten years, and then round to the nearest whole number (or multiple of 5, for higher levels). The purpose is to scale the shocks so that they are all approximately equally likely to happen. Assuming returns are normally-distributed, a given market factor has a 16% probability of exceeding a one standard deviation move in either direction over a one year period.