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Risk-Weighted Indexing
By Radu Gabudean | 26 October, 2011   

Building indices using risk components.

The investment grade corporate bonds example points to strong technical reasons why a risk-based approach to building a benchmark should be preferred: asset-based breakdowns usually result in more extreme correlations than risk-based ones. Moreover, correlations among risk types are more stable than the ones among various assets. For example, US corporates and Treasuries (or rates) were highly correlated before the 2008 credit crisis (see the second column in Figure 6) and then had a small correlation during the crisis, only for their correlation to increase sharply again afterwards.

Correlation  Between US Corporates Total Return And US Treasuries, And Between US  Corporates Rates And Spread Factors, By 24-Month Periods

Alternatively, we can separate US corporates risk into rates (or Treasury) risk and spread risk. The last column in Figure 6 shows the correlation between the risk factors associated with rates and spread risk as consistently negative. While this correlation becomes larger over the three periods, it does not reach the extremes observed with asset class correlations.

Fundamentally, risk types are related directly to various macroeconomic phenomena, such as growth and inflation, while asset classes are mostly combinations of risk types. For example, credit spread risk is largely a function of the growth prospects of the economy. However, corporate bonds are a combination of rates and spread risk. Risk types have predictable volatility levels, almost by design. To the extent that the mixture of rates and credit risk varies over time, the risk properties of corporate bonds will be more time-varying and less predictable than those of the underlying risk types (see Figure 6 and Figure 7).

Volatility Of The Corporate Bonds Asset Class And Of The Rates And Spread Risk Factors, By 24-Month Periods

A risk-based index should therefore be designed using risk-type building blocks5 as opposed to asset-based building blocks. This method results in more stable and reliable index construction.

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