Hi all,For GBM, we know how to use the Binomial tree to approximate it. For jump diffusion models, dXt=mu*dt+sigma*dBt+dJt, where Jt is compounded Poisson, how to approximate it using Binomial tree?Thanks!

you are trying to approximate an incomplete market with a complete one. Whilst not necessarily impossible you are really asking for trouble.

bQuoteOriginally posted by: mjyou are trying to approximate an incomplete market with a complete one. Whilst not necessarily impossible you are really asking for trouble.Why is the trouble? Could you please elaborate?

uQuoteOriginally posted by: losemindbQuoteOriginally posted by: mjyou are trying to approximate an incomplete market with a complete one. Whilst not necessarily impossible you are really asking for trouble.Why is the trouble? Could you please elaborate?At microscopic level, all price movements are discrete random walks,Is it possible to build a tree based on random walks?

QuoteOriginally posted by: loseminduQuoteOriginally posted by: losemindbQuoteOriginally posted by: mjyou are trying to approximate an incomplete market with a complete one. Whilst not necessarily impossible you are really asking for trouble.Why is the trouble? Could you please elaborate?At microscopic level, all price movements are discrete random walks,Is it possible to build a tree based on random walks?You want the tree to be recombining, or you get too many nodes at the end. What are the possible increments for jump diffusions ? Can you comfortably divide these into one up and one down movement ? Will the size of the up/down movement be constant, or have some other properties making the nodes recombine ? How many nodes do you get if the nodes don't recombine ? If you want to make a simulation, why would you chose trees over plain monte carlo ?