References
- L.A. Abbas-Turki and B. Lapeyre, American Options Pricing on Multi-core Graphic Cards, 2009 International Conference on Business Intelligence and Financial Engineering, Beijing, 2009, pp. 307–311.
- M. Benguigui and F. Baude, Fast American Basket Option Pricing on a Multi-GPU Cluster, Proceedings of the 22nd High Performance Computing Symposium, April, Tampa, FL, 2014, pp. 1–8.
- Cartesius webpage, https://www.surfsara.nl/systems/cartesius.
- Clogs webpage, http://sourceforge.net/projects/clogs/.
- S. Cook, CUDA Programming: A Developer's Guide to Parallel Computing with GPUs, 1st ed., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2013.
- CUB webpage, http://nvlabs.github.io/cub/.
- CUDA programming guide, http://docs.nvidia.com/cuda/cuda-c-programming-guide/.
- CUDA webpage, http://www.nvidia.com/object/cuda_home_new.html.
- CUDPP webpage, http://cudpp.github.io/.
- cuRAND webpage, https://developer.nvidia.com/curand.
- V. Cvetanoska and T. Stojanovski, Using high performance computing and Monte Carlo simulation for pricing American options, CoRR abs/1205.0106 (2012). Available at http://arxiv.org/abs/1205.0106.
- D.M. Dang, C.C. Christara, and K.R. Jackson, An efficient graphics processing unit-based parallel algorithm for pricing multi-asset American options, Concurr. Comput.: Pract. Exp. 24 (2012), pp. 849–866. Available at http://dx.doi.org/10.1002/cpe.1784. doi: 10.1002/cpe.1784
- F. Fang and C.W. Oosterlee, Pricing early-exercise and discrete barrier options by Fourier-cosine series expansions, Numer. Math. 114 (2009), pp. 27–62. doi: 10.1007/s00211-009-0252-4
- R. Farber, CUDA Application Design and Development, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2011.
- M. Fatica and E. Phillips, Pricing American Options with Least Squares Monte Carlo on GPUs, Proceedings of the 6th Workshop on High Performance Computational Finance, WHPCF'13, Denver, CO. Available at http://doi.acm.org/10.1145/2535557.2535564, ACM, New York, NY, USA, 2013, pp. 5:1–5:6.
- M.B. Haugh and L. Kogan, Pricing American options: A duality approach, Oper. Res. 52 (2004), pp. 258–270. doi: 10.1287/opre.1030.0070
- S. Jain and C.W. Oosterlee, The Stochastic Grid Bundling Method: Efficient Pricing of Bermudan Options and Their Greeks, 2013. Available at http://ssrn.com/abstract=2293942.
- B. Jan, B. Montrucchio, C. Ragusa, F.G. Khan, and O. Khan, Fast parallel sorting algorithms on GPUs, Int. J. Distrib. Parallel Syst 14 (2012), pp. 107–118. doi: 10.5121/ijdps.2012.3609
- V. Kindratenko (ed.), Numerical Computations with GPUs, Springer, 2014. Available at http://www.springer.com/us/book/9783319065472
- D.B. Kirk and W.m.W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach, Elsevier, Burlington, 2010.
- D.E. Knuth, The Art of Computer Programming, Sorting and Searching, Volume 3, 2nd ed., Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, 1998.
- H. Kobayashi, B.L. Mark, and W. Turin, Probability, Random Processes, and Statistical Analysis, Cambridge University Press, Cambridge, 2012.
- F.A. Longstaff and E.S. Schwartz, Valuing American options by simulation: A simple least-squares approach, Rev. Financ. Stud. 14 (2001), pp. 113–147. Available at http://ideas.repec.org/a/oup/rfinst/v14y2001i1p113-47.html. doi: 10.1093/rfs/14.1.113
- M.J. Misic and M.V. Tomasevic, Data sorting using graphics processing units, Telfor J. 4 (2012), pp. 43–48.
- Modern GPU webpage, http://nvlabs.github.io/moderngpu/.
- G. Pagès and B. Wilbertz, GPGPUs in computational finance: Massive parallel computing for American style options, Concurr. Comput.: Pract. Exp. 24 (2012), pp. 837–848. Available at http://dx.doi.org/10.1002/cpe.1774. doi: 10.1002/cpe.1774
- L.C.G. Rogers, Monte Carlo valuation of American options, Math. Financ. 12 (2002), pp. 271–286. doi: 10.1111/1467-9965.02010
- M.J. Ruijter and C.W. Oosterlee, Numerical Fourier Method and Second-order Taylor Scheme for Backward SDEs in Finance, Working paper, 2014.
- J. Sanders and E. Kandrot, CUDA by Example: An Introduction to General-purpose GPU Programming, Addison-Wesley, Michigan, 2011.
- N. Satish, M. Harris, and M. Garland, Designing Efficient Sorting Algorithms for Manycore GPUs, Proceedings of the 23rd IEEE International Parallel and Distributed Processing Symposium, Rome, May 2009.
- K. Thouti and S. Sathe, An OpenCL method of parallel sorting algorithms for GPU architecture, Int. J. Exp. Algorithms 3 (2012), pp. 1–8.
- Thrust webpage, http://thrust.github.io/.
- J.N. Tsitsiklis and B. Van Roy, Regression methods for pricing complex American-style options, IEEE Trans. Neural Netw. 12 (2001), pp. 694–703. doi: 10.1109/72.935083
- N.G. Ushakov, Selected Topics in Characteristic Functions (Modern Probability and Statistics), Mouton De Gruyter, Utrecht, 1999.
- VexCL webpage, http://ddemidov.github.io/vexcl/.
- N. Wilt, The CUDA Handbook: A Comprehensive Guide to GPU Programming, Addison-Wesley, Indiana, 2013.
- Wolfram Mathematica webpage, http://www.wolfram.com/mathematica/.