Read e-book online Credit Risk Modeling using Excel and VBA Gunter Lцffler PDF

By Peter N. Posch

ISBN-10: 0470031573

ISBN-13: 9780470031575

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2. 2 then add the S&P index values and the risk-free rate of return. In columns F and G, we compute the excess return on the assets and the S&P 500 (excess return is return minus risk-free rate). 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. Since equity is a call on the asset value, its riskiness depends on the riskiness of the asset value. 6). 12) are two equations with two unknowns. This system of equations does not have a closed-form solution, but we can use numerical routines to solve it.

A possible frame for building a model might look like this: 1. From economic reasoning, compile a set of variables that you believe to capture factors that might be relevant for default prediction. To give an example: the Factor ‘Profitability’ might be captured by EBIT/TA, EBITDA/TA, or Net Income/Equity. 2. Examine the univariate distribution of these variables (skewness, kurtosis, quantiles…) and their univariate relationship to default rates. 3. From step 2 determine whether there is a need to treat outliers and non-linear functional forms.

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Credit Risk Modeling using Excel and VBA Gunter Lцffler by Peter N. Posch

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