Expected RR for name conditinal to default by that date.
Full loss distribution.
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.
Default loss distribution convolution for finite non homogeneous pool
A note on the number of buckets: As it is now the code goes splitting losses into buckets from loses equal to zero to losses up to the value of the underlying basket. This is in view of a stochastic loss given default but in a constant LGD situation this is a waste and it is more efficient to go up to the attainable losses.
Extend to the multifactor case for a generic LM
Many common code with the homogeneous version, both classes perform the same work on different loss distribution types, merge and send the distribution object?