I think you can use the same approach... why not?where K is the strike of the CMS caplet.Of course you can try to replicate with

try also GNU Scientific Library

<t>Yes, you can still use a similar method even if T<t0.In this case, as you correctly pointed out, the strike P(r(T),T,t0) of the coupon bond put (payer swaption) is stochastic, because it depends on the short rate value r(T) at time T, but you can still find r* such that P(r*,T,t0) - P(r*,T,tn) - ...

<t>schizoidman,i think you should use a time dependent hazard rate function in order to try to replicate the Bloomberg results.If you choose a piecewise constant hazard rate the survival probability will be S(t) = exp(-\sum_i {h_i * Dt_i})where h_i is the constant hazard rate value for the i-th inte...

Uh? That's right of course.P = DF(0) * E_t2 [ 2 * SW(t1,t1,T) ] = 2 * DF(0) * E_t2 [ SW(t1,t1,T) ] = 2 * DF(0) * [ SW(0,t1,T) + Conv. Corr.]

<t>Of course. Because whatever definition of unexpected loss you are using (for example the loss in excess of the mean), it will be related to the shape of the loss distribution function (second and higher moments), which in turn is related to the correlation: higher is the correlation, wider is the...

<t>I think you are right. Default correlation doesn't affect the total expected value of loss on the portfolio, because the total loss is a linear combination of the individual losses: P = \sum_i {c_i * A_i} ==> L[P] = \sum_i {c_i * L(A_i)} ==> EL[P] = \sum_i {c_i * EL (A_i)}where A_i is the i-th as...

<t>In my opinion Schonbucher is an excellent (maybe the best) book for pricing models. Gregory's "Credit Derivatives: the Definite guide" is a decent collection of articles about Credit Derivatives market, structures, a few standard pricing models and legal aspects. Another book which could be of in...

For interest rates i would add Brigo, Mercurio - "Interest Rate Models: Theory and Practice", wich is probably one of the best books about models implementation, but i think you should read it after some of the other suggested titles

<t>Quotevamma = 1/2 *(vega from yesterday) * (volatility change bewteen 2 days)^2Uh??? I don't think so. Volga is the second derivative of the option price with respect to volatility :Volga = d^2P(alfa,vol)/(dvol)^2where P is the option price, vol is the volatility and alfa is a vector of other cons...

wahoo2000, maybe you're looking for Galiani's Master Thesis

You could take a look at the following articles:ML Correlation tradingSG Correlation researchHope it helps

<t>QuoteLast question : assume that I use a IR model for which I never get negative values under the risk neutral measure. When performing a change of probability measure, for instance with a negative risk premium, I will probably get negative rates. "I get negative IR" has measure 0 under the first...

fodao, now i understand your argument and i think you are definitively right about JPM approach because the condition is satisfied by definition.

<t>QuoteDL[A,B](rhoC) = DL[0,B](Y2) - DL[0,A](Y1), (2)where rhoC is the compound corr. for the tranche [A,B] and Y1 and Y2 are the Base corr. It is easy to see that (1) does not necessarily imply that (2) is satisfied with X1 and X2. This is why they are not equivalent. Regarding your other question...