abaquant.credit.distribution¶
Import path: abaquant.credit.distribution
Domain: Credit-risk analytics and fundamentals-derived credit proxies.
Purpose¶
Exact independent credit-portfolio value distributions.
When to use it¶
Use this package for transition matrices, spread-based valuation, CDS/CDO building blocks, copula simulation, tail risk, and accounting-based credit diagnostics.
Public objects¶
function:
independent_distribution— Construct the exact portfolio-value distribution under issuer independence.function:
expected_value_and_sigma— Compute issuer-level and portfolio expected values and standard deviations.
Detailed reference¶
Exact independent credit-portfolio value distributions.
Purpose¶
The module combines issuer-level destination-value distributions by iterative convolution and derives portfolio expectation and standard deviation.
Conventions¶
The exact-distribution routines use the supplied transition probabilities and assume independence across issuers.
References
[ 1 ] Merton, R. C. (1974), “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates”.
- abaquant.credit.distribution.expected_value_and_sigma(bonds_data, trans_mat)¶
Compute issuer-level and portfolio expected values and standard deviations.
- Parameters:
bonds_data (list) – Issuer or bond input records in the package credit-risk schema.
trans_mat (np.ndarray) – Credit-rating transition matrix ordered by the package rating states.
- Returns:
Positional outputs produced by the expected value and sigma calculation.
- Return type:
tuple[list[dict], dict[str, float]]
- abaquant.credit.distribution.independent_distribution(bonds_data, trans_mat)¶
Construct the exact portfolio-value distribution under issuer independence.
- Parameters:
bonds_data (list) – Issuer or bond input records in the package credit-risk schema.
trans_mat (np.ndarray) – Credit-rating transition matrix ordered by the package rating states.
- Returns:
Ordered collection produced by the independent distribution calculation.
- Return type:
list