Computational Finance on the GPU

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Backoffice • Manage risk and capital reserves for regulation – Large runs can take days to complete – Accurate results allow greater control, and hence more trades • Monitor traders’ exposure – Run intra-day • Greater accuracy requires longer run times Traders Backoffice Quants Developers
AGENDA Financial Instruments Who’s Who Modeling Algorithms Adoption Summary
Page 1 and 2: Computational <str
Page 3 and 4: AGENDA Financial Instruments Who’
Page 5 and 6: Financial Instruments Cash Instrume
Page 7 and 8: Commodities • Raw resource - Agri
Page 9 and 10: Foreign Exchange • Also: Forex or
Page 11 and 12: Example: Options • Holder has the
Page 13 and 14: Traders • Trading: - Standardised
Page 15 and 16: Quants • Develop and implement mo
Page 19: Traders • Determine price for tra
Page 23 and 24: Binomial Trees • Represent possib
Page 25 and 26: Binomial Trees
Page 27 and 28: Finite Differences • Solve the Pa
Page 29 and 30: Finite Differences - Explicit • R
Page 31 and 32: Explicit Finite Differences on the
Page 33 and 34: Monte Carlo • Sample a random wal
Page 35 and 36: Monte Carlo on the GPU Generate Ran
Page 37 and 38: Monte Carlo - Single Kernel parfor
Page 39 and 40: Software legacy • Millions of lin
Page 41 and 42: Case Study: Equity Derivatives 15 1
Page 43 and 44: Case Study: Real-time Options 1 Sam
Page 47: Resources • GTC presentations - <

Computational Finance on the GPU

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