Correlations are an essential component of a risk management and investment decision framework. They determine how risk aggregates across different asset classes and liabilities into portfolio or balance sheet risk.

Unfortunately, correlations are very complex because they have many dimensions. Calibrating risk models to realistic correlation structures can be difficult and time-consuming, especially as the dimensions of the models increase. Practitioners are then often forced to follow a “partial approach” in which they calibrate models on an application-specific subset of the correlation dimensions.

Although understandable, such a partial approach to correlation modeling is inefficient and inconsistent. In this paper we discuss the various dimensions of correlations and illustrate how these can be mastered with the help of well-designed and calibrated risk models.

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