Name
Abstract | ||
|---|---|---|
In the Black-Scholes-Merton model, as well as in more general stochastic models in finance, the price of an American option solves a system of partial differential variational inequalities. When these inequalities are discretized, one obtains a linear complementarity problem that must be solved at each time step. This talk presents an algorithm for the solution of these types of linear complementarity problems that is significantly faster than the methods currently used in practice and can exploit parallelism. The new algorithm is a two-phase method that combines the active-set identification properties of the projected Gauss-Seidel (or SOR) iteration with the second-order acceleration of a reduced-space phase. We present numerical results that illustrate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645413.1645414 | SC-WHPCF |
Keywords | Field | DocType |
parallel algorithm,partial differential variational inequality,american option,black-scholes-merton model,projected gauss-seidel,general stochastic model,reduced-space phase,numerical result,active-set identification property,new algorithm,linear complementarity problem,second order,gauss seidel,stochastic model,variational inequality | Complementarity (molecular biology),Mathematical optimization,Parallel algorithm,Computer science,Partial derivative,Complementarity theory,Stochastic modelling,Linear complementarity problem,Mixed complementarity problem,Variational inequality | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Nocedal | 1 | 3276 | 301.50 |