Returns the probability of the default loss values given by the method lossPoints.
Expected RR for name conditinal to default by that date.
This method should be called at the end of non-const methods or when the programmer desires to notify any changes.
Returns the probaility of having a given or larger number of defaults in the basket portfolio at a given time.
Probability of the tranche losing the same or more than the fractional amount given.
The passed lossFraction is a fraction of losses over the tranche notional (not the portfolio).
Probabilities for each of the (remaining) basket elements in the pool to have defaulted by time d and at the same time be the Nth defaulting name to default in the basket. This method is oriented to default order dependent portfolio pricing (e.g. NTDs) The the probabilities ordering in the vector coincides with the pool order.
Binomial Defaultable Basket Loss Model
Models the portfolio loss distribution by approximatting it to an adjusted binomial. Fits the two moments of the loss distribution through an adapted binomial approximation. This simple model allows for portfolio inhomogeneity with no excesive cost over the LHP.\par See:\par Approximating Independent Loss Distributions with an Adjusted Binomial Distribution , Dominic O'Kane, 2007 EDHEC RISK AND ASSET MANAGEMENT RESEARCH CENTRE \par Modelling single name and multi-name credit derivatives Chapter 18.5.2, Dominic O'Kane, Wiley Finance, 2008 \par The version presented here is adaptated to the multifactorial case by computing a conditional binomial approximation; notice that the Binomial is stable. This way the model can be used also in risk management models rather than only in pricing. The copula is also left undefined/arbitrary. \par LLM: Loss Latent Model template parameter able to model default and loss.\par The model is allowed and arbitrary copula, although initially designed for a Gaussian setup. If these exotic versions were not allowed the template parameter can then be dropped but the use of random recoveries should be added in some other way.
untested/wip for the random recovery models.
integrate with the previously computed probability inversions of the cumulative functions.