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Exposure at default or (EAD) is a parameter used in the calculation of economic capital or regulatory capital under Basel II for a banking institution. It can be defined as the gross exposure under a facility upon default of an obligor. [1] [2] Outside of Basel II, the concept is sometimes known as Credit Exposure (CE). It represents the ...
Instead of 5% defaulting, say 10% default, largely due to the fact the LGD has catastrophically risen. To accommodate for that type of situation a much larger expected loss needs to be calculated. This is the subject to considerable research at the national and global levels as it has a large impact on the understanding and mitigation of ...
Loss given default or LGD is the share of an asset that is lost if a borrower defaults. It is a common parameter in risk models and also a parameter used in the calculation of economic capital, expected loss or regulatory capital under Basel II for a banking institution. This is an attribute of any exposure on bank's client.
Examples of static characteristics are industry for wholesale loans and origination "loan to value ratio" for retail loans. An unstressed PD is an estimate that the obligor will default over a particular time horizon considering the current macroeconomic as well as obligor specific information.
Banks can determine their own estimation for some components of risk measure: the probability of default (PD), loss given default (LGD), exposure at default (EAD) and effective maturity (M). For public companies, default probabilities are commonly estimated using either the "structural model" of credit risk proposed by Robert Merton (1974) or ...
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation: >>>
PFE is the "Potential Future Exposure" to the counterparty: per asset class, trade-"add-ons" are aggregated to "hedging sets", with positions allowed to offset based on specified correlation assumptions, thereby reducing net exposure; these are in turn aggregated to counterparty "netting sets"; this aggregated amount is then offset by the ...
Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core ...