Python Homework 5: Financial Credit Rating Instruction: Please upload your jupyter notebook on GauchoSpace with filename "PythonHW5 YOURPERMNUMBER.ipynb" In Mathematical Finance, Markov chains are typically used to model the default risk of a company or country (more specifically, the default of a company's or country's liability like a corporate or government bond see PSTAT 171 and PSTAT 170) Rating agencies (like Standard& Poor's, Moody's, Fitch) rate the financial stability of a company and classify them according to different classes. A possible classification may range from 'AAA for debitors with a very good credit rating to 'CCC for debitors which are very likely to delay in paying a debt; and 'D' for those debitors which can't satisfy their financial labilities anymore (in other words, they are default). The yearly credit rating of a company can be modeled as a Markov chain (Xn)n01, with state space where Xn represents the credit rating class of a company at the n-th year. The transition probabilities are given by AAA 92,07 7,09 0,63 0,15 0,06 0,00 0,00 0,00 AA 0,62 90, 84 7,76 0,59 0,06 0,10 0,02 0,01 A 0,05 2,09 91,385,79 0,44 0,16 0,04 0,05 0,03 0, 21 4,10 89,37 4,82 0,86 0, 24 0,37 BB 0,03 0,08 0,40 5,54 83,24 8, 15 1,11 1,45 B 0,00 0,08 0,27 0,34 5,39 82,41 4,92 6,59 CCC 0, 10 0, 00 0,29 0,58 55 10,54 52, 80 34,14 D 0,00 0,00 0,00 0,00 0,00 0,00 0,00 100,0 Python Homework 5: Financial Credit Rating Instruction: Please upload your jupyter notebook on GauchoSpace with filename "PythonHW5 YOURPERMNUMBER.ipynb" In Mathematical Finance, Markov chains are typically used to model the default risk of a company or country (more specifically, the default of a company's or country's liability like a corporate or government bond see PSTAT 171 and PSTAT 170) Rating agencies (like Standard& Poor's, Moody's, Fitch) rate the financial stability of a company and classify them according to different classes. A possible classification may range from 'AAA for debitors with a very good credit rating to 'CCC for debitors which are very likely to delay in paying a debt; and 'D' for those debitors which can't satisfy their financial labilities anymore (in other words, they are default). The yearly credit rating of a company can be modeled as a Markov chain (Xn)n01, with state space where Xn represents the credit rating class of a company at the n-th year. The transition probabilities are given by AAA 92,07 7,09 0,63 0,15 0,06 0,00 0,00 0,00 AA 0,62 90, 84 7,76 0,59 0,06 0,10 0,02 0,01 A 0,05 2,09 91,385,79 0,44 0,16 0,04 0,05 0,03 0, 21 4,10 89,37 4,82 0,86 0, 24 0,37 BB 0,03 0,08 0,40 5,54 83,24 8, 15 1,11 1,45 B 0,00 0,08 0,27 0,34 5,39 82,41 4,92 6,59 CCC 0, 10 0, 00 0,29 0,58 55 10,54 52, 80 34,14 D 0,00 0,00 0,00 0,00 0,00 0,00 0,00 100,0