1. |
Weak approximation of SDEs for tempered distributions and applications (Peer-reviewed) Yuga Iguchi, Toshihiro Yamada
Advances in Computational Mathematics (to appear) 2022.5 |
2. |
A weak approximation method for irregular functionals of hypoelliptic diffusions (Peer-reviewed) Naho Akiyama, Toshihiro Yamada
Applied Numerical Mathematics 2022.2 |
3. |
A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for Deep BSDE solver (Peer-reviewed) Akihiko Takahashi, Yoshifumi Tsuchida, Toshihiro Yamada
Journal of Computational Physics 2022.1 |
4. |
A Gaussian Kusuoka-approximation without solving random ODEs (Peer-reviewed) Toshihiro Yamada
SIAM Journal on Financial Mathematics 2022.1 |
5. |
A deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to CVA computation (Peer-reviewed) Riu Naito, Toshihiro Yamada
IEEE CIFEr 2022 2022 |
6. |
Deep weak approximation of SDEs: a spatial approximation scheme for solving Kolmogorov equations (Peer-reviewed) Riu Naito, Toshihiro Yamada
International Journal of Computational Methods 2022 |
7. |
Numerical methods for backward stochastic differential equations: a survey
Jared Chessari, Reiichiro Kawai, Yuji Shinozaki, Toshihiro Yamada
arXiv 2022 |
8. |
A high order weak approximation for jump-diffusions using Malliavin calculus and operator splitting (Peer-reviewed) Naho Akiyama, Toshihiro Yamada
Monte Carlo Methods and Applications 2022 |
9. |
Deep Asymptotic Expansion: application to financial mathematics (Peer-reviewed) Yuga Iguchi, Riu Naito, Yusuke Okano, Akihiko Takahashi, Toshihiro Yamada
IEEE CSDE 2021 (to appear) 2021.12 |
10. |
A higher order weak approximation of McKean-Vlasov type SDEs (Peer-reviewed) Riu Naito, Toshihiro Yamada
BIT Numerical Mathematics 2021.6 |
11. |
High order weak approximation for irregular functionals of time-inhomogeneous SDEs (Peer-reviewed) Toshihiro Yamada
Monte Carlo Methods and Applications 2021 |
12. |
Acceleration of automatic differentiation of solutions to parabolic partial differential equations: a higher order discretization (Peer-reviewed) Kimiki Tokutome, Toshihiro Yamada
Numerical Algorithms Vol.86,pp.593-635 2021 |
13. |
A second order discretization for degenerate systems of stochastic differential equations (Peer-reviewed) Yuga Iguchi, Toshihiro Yamada
IMA Journal of Numerical Analysis 2021 |
14. |
Discrete Bismut formula: Conditional integration by parts and a representation for delta hedging process (Peer-reviewed) Naho Akiyama, Toshihiro Yamada
Risk and Decision Analysis 2021 |
15. |
Operator splitting around Euler-Maruyama scheme and high order discretization of heat kernels (Peer-reviewed) Yuga Iguchi, Toshihiro Yamada
ESAIM: Mathematical Modelling and Numerical Analysis Vol.55,pp.323-367 2021 |
16. |
An acceleration scheme for deep learning-based BSDE solver using weak expansions (Peer-reviewed) Riu Naito, Toshihiro Yamada
International Journal of Financial Engineering 2020 |
17. |
A second order discretization with Malliavin weight and Quasi Monte Carlo method for option pricing (Peer-reviewed) Toshihiro Yamada, Kenta Yamamoto
Quantitative Finance Vol.20,No.11 2020 |
18. |
Second order discretization of Bismut-Elworthy-Li formula: application to sensitivity analysis (Peer-reviewed) Toshihiro Yamada, Kenta Yamamoto
SIAM/ASA Journal on Uncertainty Quantification Vol.7,No.01,pp.143-173 2019 |
19. |
A control variate method for weak approximation of SDEs via discretization of numerical error of asymptotic expansion (Peer-reviewed) Yusuke Okano, Toshihiro Yamada
Monte Carlo Methods and Applications Vol.25,No.3 2019 |
20. |
A second-order discretization for forward-backward SDEs using local approximations with Malliavin calculus (Peer-reviewed) Riu Naito, Toshihiro Yamada
Monte Carlo Methods and Applications Vol.25,No.4 2019 |
21. |
An arbitrary high order weak approximation of SDE and Malliavin Monte Carlo: analysis of probability distribution functions (Peer-reviewed) Toshihiro Yamada
SIAM Journal on Numerical Analysis Vol.57,No.2,pp.563-591 2019 |
22. |
A third-order weak approximation of multidimensional Ito stochastic differential equations (Peer-reviewed) Riu Naito, Toshihiro Yamada
Monte Carlo Methods and Applications Vol.25,No.2,pp.97-120 2019 |
23. |
Weak Milstein scheme without commutativity condition and its error bound (Peer-reviewed) Toshihiro Yamada
Applied Numerical Mathematics Vol.131,No.2018 (September),pp.95-108 2018.9 |
24. |
A second order weak approximation of SDEs using Markov chain without Levy area simulation (Peer-reviewed) Toshihiro Yamada, Kenta Yamamoto
Monte Carlo Methods and Applications Vol.24,No.04,pp.289-308 2018 |
25. |
A weak approximation with Malliavin weights for local stochastic volatility model (Peer-reviewed) Toshihiro Yamada
International Journal of Financial Engineering Vol.04,No.01 2017
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26. |
A higher order weak approximation scheme of multidimensional stochastic differential equations using Malliavin weights (Peer-reviewed) Toshihiro Yamada
Journal of Computational and Applied Mathematics Vol.321,No.September 2017,pp.427-447 2017
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27. |
A weak approximation with asymptotic expansion and multidimensional Malliavin weights (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
Annals of Applied Probability Vol.26,No.2,pp.818-856 2016
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28. |
An asymptotic expansion for forward–backward SDEs: a Malliavin calculus approach (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
Asia-Pacific Financial Markets Vol.23,No.4,pp.337-373 2016 |
29. |
A formula of small time expansion for Young SDE driven by fractional Brownian motion (Peer-reviewed) Toshihiro Yamada
Statistics and Probability Letters Vol.101,pp.64-72 2015
|
30. |
On error estimates for asymptotic expansions with Malliavin weights: Application to stochastic volatility model (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
Mathematics of Operations Research Vol.40,No.3,pp.513-451 2015
|
31. |
A small noise asymptotic expansion for Young SDE driven by fractional Brownian motion: A sharp error estimate with Malliavin calculus (Peer-reviewed) Toshihiro Yamada
Stochastic Analysis and Applications Vol.33,No.5,pp.882-902 2015 |
32. |
A Malliavin calculus approach with asymptotic expansion in computational finance (Peer-reviewed) Toshihiro Yamada
Ph.D. Thesis, The University of Tokyo 2015 |
33. |
An asymptotic expansion of forward-backward SDEs with a perturbed driver (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
International Journal of Financial Engineering Vol.2,No.2 2015 |
34. |
A semigroup expansion for pricing barrier options (Peer-reviewed) Takashi Kato, Akihiko Takahashi, Toshihiro Yamada
International Journal of Stochastic Analysis Vol.2014,pp.1-15 2014
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35. |
Strong convergence for Euler-Maruyama and Milstein schemes with asymptotic method (Peer-reviewed) Hideyuki Tanaka, Toshihiro Yamada
International Journal of Theoretical and Applied Finance Vol.17,No.2,pp.1450014-1-1450014-22 2014
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36. |
An asymptotic expansion formula for up-and-out barrier option price under stochastic volatility model (Peer-reviewed) Takashi Kato, Akihiko Takahashi, Toshihiro Yamada
JSIAM Letters Vol.5,pp.17-20 2013
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37. |
Pricing discrete barrier options under stochastic volatility (Peer-reviewed) Kenichiro Shiraya, Akihiko Takahashi, Toshihiro Yamada
Asia-Pacific Financial Markets Vol.19,No.3,pp.205-232 2012.9
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38. |
A remark on approximation of the solutions to partial differential equations in finance (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
Recent Advances in Financial Engineering 2011 2012.6 |
39. |
An asymptotic expansion with push-down of Malliavin weights (Peer-reviewed) Akihiko Takahashi, Toshihiro Yamada
SIAM Journal on Financial Mathematics Vol.3,No.1,pp.95-136 2012.1
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