Portfolio managers have long worried that discrepancies between risk and alpha factors may somehow detract from the performance of their optimized portfolios. This paper presents a comprehensive overview of alpha-risk factor alignment and its consequences, showing how penalizing the residual alpha may help reduce the unintended bets resulting from misalignment. However, we also illustrate that correcting for misalignment may not always be necessary and can sometimes be...

Following the “quant meltdown” of August 2007, market observers became concerned that quant strategies were leading to crowded trades. This paper analyzes the impact that a risk model used in portfolio construction has on manager crowding by identifying the drivers of crowding and by illustrating their impact.  A risk model’s effect on manager crowding depends, in part, on how alphas used by different managers are related to each other, and to the risk model factors. We...

This Model Insight introduces the MSCI Bond Liquidity Measure (BLM), a model-based estimate of bond bid-ask spreads for a broad universe of quoted and non-quoted bonds. The BLM provides both a liquidity scoring metric and a way to quantify potential transaction costs. Applications of BLM include portfolio construction, risk control, risk limits, regulatory compliance, and liquidity provisioning.  Furthermore, BLM opens the way  for the consistent assessment of liquidity...

Many portfolio managers use multi-factor models, but not all factor models are equally effective in forecasting risk.

Focused on US Buyouts and Ventures, the new Barra Private Equity Model (PEQ1) attributes risk to intuitive public equity and private equity investment type factors, reflects changes in risk in a more timely fashion than traditional methods, and captures the relationship between public and private equity. 

Portfolio managers have long suspected that the risk forecast of an optimized portfolio tends to be optimistic. Many have identified the culprit as estimation error in the covariance matrix. Forecasts based on historical asset covariance matrices are particularly sensitive to this error. The bias is reduced dramatically by using a factor model. Even so, factor models still tend to under-forecast the risk of optimized portfolios, especially the risk coming from factors. In this paper, we show...

Portfolio managers have long suspected that the risk forecast of an optimized portfolio tends to be optimistic. Many have identified the culprit as estimation error in the covariance matrix. Forecasts based on historical asset covariance matrices are particularly sensitive to this error. The bias is reduced dramatically by using a factor model. Even so, factor models still tend to under-forecast the risk of optimized portfolios, especially the risk coming from factors. In this paper, we show...

In this paper, we study the relationship between private and public real estate in the US and UK markets and demonstrate a strong link between returns in each of these markets. To uncover these relationships, we correct for appraisal smoothing and properly account for the lead-lag relationship between public and private returns. We find that public returns...

In this study, our goal is to adapt mean-variance optimization to produce active portfolios with less exposure to extreme losses than normal optimized portfolios. Specifically, we illustrate how extreme risk can be incorporated into portfolio construction in a straightforward way by constraining the shortfall beta of the optimal portfolio. Our simple empirical examples suggest that constraining shortfall beta may offer some downside protection in turbulent periods without sacrificing...

When there is noise in a covariance matrix, portfolio optimization tends to produce portfolios for which the risk forecasts are underestimates of the true risk. In this paper, we take a closer look at the connection between estimation error and the underestimation of the risk of optimized portfolios. We pay special attention to the case in which returns have a known factor structure. There, the bias in optimization can be reduced dramatically by using a covariance matrix based on a factor...

This paper analyzes the impact of constraints on portfolio return and risk, extending the insights of previous research in this area. We show that constraints move a manager's portfolio away from the optimal unconstrained portfolio in two ways. First, they may rein in or increase the risk of the portfolio without impairing its information ratio. Second, they may force the portfolio to take unwanted bets that incur risk but yield no return. As a result, a constrained portfolio consists of...

Misalignment between alpha and risk factors may create unintended bets in optimized portfolios, as shown analytically in Lee and Stefek (2008).  In a March research insight, we introduced a way to mitigate this issue by penalizing the portion of the alpha not related to the risk factors, the 'residual alpha.' Here, we further illustrate this method with two signals commonly used by portfolio managers. The potential improvement from this method depends on the strategy in question, in...

The misalignment of alpha and risk factors may result in inadvertent and unwanted bets that may hamper performance. Lee and Stefek (2008) show that better aligning risk factors with alpha factors may improve the information ratio of optimized portfolios. They propose four ways of modifying a risk model to reduce misalignment. Here, we discuss one way to mitigate these problems by modifying the optimization process, itself. The objective function is modified to include a penalty term on the...

Traditional asset allocation approaches have not provided the full potential of diversification. Here, we introduce a different approach and look at structuring portfolios using risk premia within the traditional asset classes or from systematic trading strategies. We confirm the potential benefits of such an approach by comparing a typical 60/40 equity/fixed income allocation with an equal weighted allocation across eleven risk premia.

Portfolio managers worry that discrepancies between risk and alpha factors may create unintended biases in their optimized portfolios. We analyze the ramifications of using different factor models of risk and alpha in portfolio optimization and show that aligning risk factors with alpha factors may improve the information ratio of optimized portfolios, even if doing so lowers the overall accuracy of risk forecasts. We discuss ways of modifying a risk model that may help remedy these problems.

The Barra Integrated Model (BIM) is a multi-asset class model for forecasting the asset and portfolio level risk of global equities, bonds, currencies, and commodities. The model uses innovative methods to couple broad asset coverage with the detailed analysis of Barra's models that focus on particular markets. This makes it suitable for a wide range of investment purposes, from conducting an in-depth analysis of a single-country portfolio to understanding the risk profile of a broad set of...

In the Autumn 2002 edition of Horizon, we introduced our new framework for global risk analysis, the Barra Integrated Model. This multi asset class framework was developed with two goals in mind: breadth of coverage and in-depth analysis. Risk analysis using the Integrated Model allows investment professionals to capture the detail available with local market models while retaining the broad perspective of a global, multi asset class model. To achieve these objectives, we first built models...

Barra has recently released the Barra Integrated Model, a multi-asset class model for forecasting the asset and portfolio level risk of global equities, bonds, and currencies. The model uses innovative methods to couple broad asset coverage with the detailed analysis of Barra's models that focus on particular markets. This makes it suitable for a wide range of investment purposes, from conducting an in-depth analysis of a single-country portfolio to understanding the risk profile of a broad...

The Barra Integrated Model is a multi-asset class model for forecasting the asset and portfolio level risk of global equities, bonds and currencies. The model uses innovative methods to couple broad asset coverage with the detailed analysis of Barra's models that focus on particular markets. This makes it suitable for a wide range of investment purposes, from conducting an in-depth analysis of a single-country portfolio to understanding the risk profile of a broad set of international...

The Barra Integrated Model is a multi-asset class model for forecasting the asset and portfolio level risk of global equities, bonds and currencies.  The model uses innovative methods to couple broad asset coverage with the detailed analysis of Barra's models that focus on particular markets.  This makes it suitable for a wide range of investment purposes, from conducting an in-depth analysis of a single-country portfolio to understanding the risk profile of a broad set of...