By C.Bluhm, L.Overbeck & C.Wagner

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**Sample text**

For example, let A be DaimlerChrysler and B stand for BMW. 5. In our example we could think of the automotive industry as an underlying factor having significant impact on the economic future of the companies A and B. Of course there are probably some more underlying factors driving the riskiness of A and B. For example, DaimlerChrysler is to a certain extent also influenced by a factor for Germany, the United States, and eventually by some factors incorporating Aero Space and Financial Companies.

Rm ) can conveniently be written by means of underlying factors. Note that for computational purposes Equation (1. 25) is the most convenient one, because the underlying factors are independent. In contrast, for an economic interpretation and for scenario analysis one would rather prefer Equation (1. 22), because the industry and country indices are easier to interpret than the global factors constructed by ©2003 CRC Press LLC PCA. In fact, the industry and country indices have a clear economic meaning, whereas the global factors arising from a PCA are of synthetic type.

Assume we have two firms A and B which are positively correlated. For example, let A be DaimlerChrysler and B stand for BMW. 5. In our example we could think of the automotive industry as an underlying factor having significant impact on the economic future of the companies A and B. Of course there are probably some more underlying factors driving the riskiness of A and B. For example, DaimlerChrysler is to a certain extent also influenced by a factor for Germany, the United States, and eventually by some factors incorporating Aero Space and Financial Companies.

### An Introduction to Credit Risk Modeling by C.Bluhm, L.Overbeck & C.Wagner

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