Abstract
Risk factor models are now widely used by fund managers to construct portfolios and assess both return and risk based on the behaviour of common risk factors to which the portfolios are exposed. However, fund managers often have subjective views on these risk factors that they may wish to incorporate into their asset allocation strategies.
This study introduces an extension of the Black-Litterman model that allows views to be applied to risk factors rather than individual assets, greatly simplifying the process since the number of factors is typically much smaller than the number of assets in a portfolio.
The concept of risk premia is central to portfolio allocation, but is typically assessed at the asset level. In our framework, risk premia are formulated and analyzed at the factor level. This theoretical advance allows the manager to calculate factor risk premia, formulate views based on these premia, and incorporate them into the portfolio optimization process to create an adjusted portfolio that is consistent with the manager’s expectations.
This new framework has many applications. It allows fund managers to analyze the market’s implied risk premia and identify the key drivers of market returns. In addition, the model facilitates comparisons between an actively managed portfolio and its benchmark by calculating how both are priced and identifying the factors that differentiate them.
The approach can also be extended to incorporate economic factors, such as economic indicators or narratives, and can be applied to macroeconomic factor-mimicking portfolios. This article examines examples of each of these applications and analyzes the results obtained.
Finally, given that the model involves several parameters that can be difficult to define, we provide practical guidance and demonstrate how varying these parameters can affect the final.
Keywords: Factor model, risk premium, Black-Litterman model, minimum-variance portfolio, active management, tactical asset allocation.
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