worked out thanks)))

<t>Hi I am pricing an option using GOP as numeraire. i havevalue of the option at time t is V(t) = S*(t) E{ V(T)/S*(T) | F(t)}where: S* is the GOP, V(T) is a well-defined payoff of the option at maturity T and F(t) is filtration up to time t and expectation wrt real world probability measure P where...

<t>Thank you for the replies and for the referenced papers.I was looking at two papers primarily: the Rockafellar&Uryasev(2000) which @eh has mentioned and Deb et al (2000a,b). So, I have access to methodology, just was hoping to see if anyone knew of a package/library which was already implemen...

<t>HiWondering if anyone is aware of an implemented portfolio optimisation algorithm which would deal with a VaR objective function.The purpose is to produce a Mean - Value at Risk efficient frontier for a portfolio of non-Normally distributed underlying assets (heavy kurtosis and skew so cant get a...

<t>HIhave a look at:Simon, S. (2005) Can Interest Rate Dynamics Save Structural Models? -- for an example of a structural modeland maybe:Bernard, C., Le Courtois, O. and Quittard-Pinon, F. (2008) PricingDerivatives with Barriers in a Stochastic Interest Rate Environment Journal ofEconomic Dynamics &...

Excellent, will def try to implement this!! my dimension is only small, so suits the purpose perfectlyThanks a lot!!! (btw, takes 130 milliseconds in 6 dimensions)

<t>HIis it possible to represent the constraint(1) SUM{x(i)*I(x(i)>0)}<1in the form(2) SUM{a(i)*x(i)}<bwhere x(i) are variables on [-1,1], a(i) and b are real, and we are allowed to represent (1) with several equations of the type (2), and I(.) is an indicator function for event (.).if this is impos...

fixed.thanks Orbit and everyone who's looked at this..trouble was in the notation,,, so diag(max(eigen_i,0)) meanssay, in R,diag(eigen_i*(eigen_i>0))for a vector of eigenvalues eigen_i

<t>here is the paper:[link to paper]maybe i am misreading the notation?i am interpreting max(eigenvalues,0) as "the maximum of the largest eigenvalue and zero" and diag(max(eigenvalues,0)) as the diagonal matrix with *that* value on the diagonal(so say for negative maximum eigenvalue you will get a ...

<t>hello,i am looking at a published paper which states that for a symmetric square matrix A and its spectral decomposition A = Q*D*t(Q) : D = diag(eigenvalues) and Q orthogonal,we should have "A-" = Q*diag(max(eigenvalues,0))*t(Q) and "A+" = Q*diag(min(eigenvalues,0))*t(Q)with "A-" + "A+" = Ahoweve...

<t>thank you manolom and list,@manolom : so you rkn the differential for the ito integral "Integral[0,t]{a(s)dW(s)}" is a(t)dW(t)? sorry im like a total noob in stochastic calculus..@list: thanks! im still looking for the formula, might give my librray a call tomorow for that Lazrieva and Toronjadze...

<t>hi, my question is -- if we differentiate a function of an Ito process X(t) and t with Ito's lemma, can that function involve stochastic terms? can these stochastic terms be correlated to X(t)?specifically, i want to differentiate f:f{X(t),t} = X(t) + integral[0,t]{a(t)dW} + b(t)where a(t) and b(...

nice thank you!!!

nice thank you!!!

hello, i have a small (general) question, -- if the brownian motions W1 and W2 have zero correlation, are the Ito integrals wrt these BM's correlated? why or why not?thanks!!