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Volumn 20, Issue 2, 2014, Pages 145-165

A numerical algorithm for fully nonlinear HJB equations: An approach by control randomization

Author keywords

Backward stochastic differential equations; control randomization; empirical regressions; HJB equation; Monte Carlo; uncertain volatility

Indexed keywords


EID: 84902305162     PISSN: 09299629     EISSN: 15693961     Source Type: Journal    
DOI: 10.1515/mcma-2013-0024     Document Type: Article
Times cited : (66)

References (18)
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    • (2012) Numerical Methods in Finance, Springer Proceedings in Mathematics 12 , pp. 215-255
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    • Fahim, A.1    Touzi, N.2    Warin, X.3
  • 9
    • 85083150009 scopus 로고    scopus 로고
    • Feynman-Kac representation for Hamilton-Jacobi-Bellman IPDE
    • to appear
    • I. Kharroubi and H. Pham, Feynman-Kac representation for Hamilton-Jacobi-Bellman IPDE, Annals of Probability, to appear.
    • Annals of Probability
    • Kharroubi, I.1    Pham, H.2
  • 10
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    • Rate of convergence of an empirical regression method for solving generalized backward stochastic differential equations
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  • 11
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    • Valuing American options by simulation: A simple least-squares approach
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    • (2001) Review of Financial Studies , vol.14 , Issue.1 , pp. 113-147
    • Longstaff, F.A.1    Schwartz, E.S.2
  • 16
    • 0035391083 scopus 로고    scopus 로고
    • Regression methods for pricing complex American-style options
    • DOI 10.1109/72.935083, PII S1045922701050238
    • J. Tsitsiklis and B. Van Roy, Regression methods for pricing complexAmerican-style options, IEEE Transactions on Neural Networks 12 (2001), 694-703. (Pubitemid 32732812)
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    • Tsitsiklis, J.N.1    Van Roy, B.2
  • 18
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    • Quantitative error estimates for a least-squares Monte Carlo algorithm for American option pricing
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    • Zanger, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.