Credit-Risk Modelling Libraries
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License: GNU General Public License v3.0
Credit-Risk Modelling Libraries
License: GNU General Public License v3.0
If anyone could add crPlusMultifactor call to the codebase, that would be great. I'm a bit perplexed about wMat parameter usage in particular. Thank you.
Hi
Import threshold and mertonmodel modules cause dead kernel in jupyter.
I cannot get most of the exercises to run as I get dead kernel likely related to above issue.
I am using python 3.7 and have installed rpy2 and r-GIGrvg as well as all the other packages and have isntalled R in the environment. I have tried on two separate pc with anaconda and get this same problem.
Any suggestions please?
Hi David,
Files exposures.npy and defaultProbabilites.npy are not functioning properly. Hope you see this and can get them updated.
Thank you
File "D:\Anaconda3\lib\site-packages\rpy2\robjects\packages.py", line 471, in importr
raise PackageNotInstalledError(
PackageNotInstalledError: The R package "GIGrvg" is not installed.
Hi Dr. Bolder, how are you? I hope doing well.
Recently, I bought the book "Credit-Risk Modelling - Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python" written by you. First of all, I'd like to say this book is awesome! Thanks so much for your contribution!.
I have tried to develop something in Python aiming to calculate the transition matrix for a specific sample considering the cohort and hazard rate approaches, and also some confidence levels using bootstrap. Unfortunately, I'm not a Python expert, and at this point I wasn't able to accomplish it in Python.
I'm sending as an attached file the output results achieved through a Visual Basic for Applications function. I've been trying to achieve the same results using the Python. I would be so grateful if you are able to send me a code developed in Python in order to achieve the same results.
Input_RatingTransitionMatrix.txt
Output_RatingTransitionMatrix.xlsx
Thanks in advance.
Best regards,
Raphael
def computeRiskMeasures(M,lossDistribution,alpha):
expectedLoss = np.mean(lossDistribution)
unExpectedLoss = np.std(lossDistribution)
expectedShortfall = np.zeros([len(alpha)])
var = np.zeros([len(alpha)])
for n in range(0,len(alpha)):
expectedShortfall[n] = np.mean(lossDistribution[np.ceil(alpha[n](M-1)).astype(int):M-1])
var[n] = lossDistribution[np.ceil(alpha[n](M-1)).astype(int)]
return expectedLoss,unExpectedLoss,var,expectedShortfall
computeRiskMeasures(100,lossDistribution,0.05)
Is Alpha like 0.01 0.05 ?
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