By Gunter Loeffler, Peter N. Posch
Although i'm really not an Excel specialist, this booklet is sort of worthy in developing versions. due to Wiley Finance and Amazon.
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Note, however, that the estimated coefficients differ significantly because the two distributions have different variances. When comparing logit and probit models estimated on the same data set, you should compare default probability estimates or other information which is not affected by scaling. Marginal effects Scenario analysis is an intuitive way of understanding the impact of individual variables. An analytical approach would be to calculate the marginal effect of a variable. In linear Credit Risk Modeling using Excel and VBA 25 models the marginal effect is equal to the coefficient.
2 Using estimated asset values and the CAPM to derive an estimate of the drift rate of asset returns By regressing the asset value returns on S&P 500 returns, we obtain an estimate of the assets’ beta. This is done in cell I5 with the function SLOPE. 6%. 2). The drift rate is for logarithmic returns. 046). Now that we have estimates of the asset volatility, the asset value and the drift rate, we can compute the default probability. 3. 34%. 11) for various dates t. 11) for the current date t only, and introduce another equation that also contains the two unknowns.
Summing over the default variable yields the number of defaults as defaults are coded as 1. In an analogous way, we determine the number of observations. We just replace SUMIF by COUNTIF. What does the graph tell us? Apparently, it is only for very low values of ME/TL that a change in this variable impacts default risk. Above the 20th percentile, there are many ranges with zero default rates, and the ones that see defaults are scattered in a way that does not suggest any systematic relationship.
Credit Risk Modeling using Excel and VBA by Gunter Loeffler, Peter N. Posch