随机最优控制理论研究方向:
[1]Han Y C, Wang C F. A stochastic maximum principle for general controlled systems driven by mixed Brownian motions. Mathematical Control and Related Fields, 2026,17:190-207.
[2]Han Y C, Li Y H. Maximum principle for discrete-time control systems driven by fractional noises and related backward stochastic difference equations. Systems Control Lett., 2025, 204:106202.
[3]Chen L Y, Han Y C, Li Y H. Stochastic maximum principle for a generalized Volterra control system. Discrete Contin. Dyn. Syst. Ser. S, 2025, 18(11):3482-3498.
[4]Han Y C, Li Y H. Stochastic maximum principle for control systems with time-varying delay. Systems Control Lett., 2024, 191:105864.
[5]Han Y C, Song Q S, Wang G. Exit problems as the generalized solutions of Dirichlet problems. SIAM J. Control Optim., 2019, 57(4):2392-2414.
[6]Han Y C, Sun Y F. Stochastic linear quadratic optimal control problem for systems driven by fractional Brownian motions. Optimal Control Appl. Methods, 2019, 40(5):900-913.
[7]Han Y C, Sun Y F. Solutions to BSDEs driven by both fractional Brownian motions and the underlying standard Brownian motions. Acta Math. Sci. Ser. B (Engl. Ed.), 2018, 38(2):681-694.
[8]Han Y C, Hu Y Z, Song J. Maximum principle for general controlled systems driven by fractional Brownian motions. Appl. Math. Optim., 2013, 67(2):279-322.
[9]Han Y C, Peng S G, Wu Z. Maximum principle for backward doubly stochastic control systems with applications. SIAM J. Control Optim., 2010, 48(7):4224-4241.
金融数学与人工智能研究方向:
[1] Han Y C, Wang W Y, Zhang D W. The GARCH model driven by fractional Brownian motion. Applied Stochastic Models in Business and Industry,2026, 42(2): e70071.
[2] Han Y C, Zheng X D. A deep learning method for pricing high-dimensional American-style options via state-space partition. Comput. Appl. Math., 2024, 43(3):152.
[3] Han Y C, Zhang F T. Pricing fixed income derivatives under a three factor CIR model with unspanned stochastic volatility.Review of Derivatives Research, 2024, 1-17.
[4] Han Y C, Zhang F T, Liu X Y. An approach to capital allocation based on mean conditional value-at-risk.Journal of Risk, 2023, 25(6), 53-71.
[5] Han Y C, Li N. A new deep neural network algorithm for multiple stopping with applications in options pricing. Commun. Nonlinear Sci. Numer. Simul., 2023, 117:106881.
[6] Han Y C, Zheng X D. Approximate pricing of derivatives under fractional stochastic volatility model. ANZIAM J., 2023, 65(3):229-247.
[7] Han Y C, Liu C Y, Song Q S. Pricing double volatility barriers option under stochastic volatility. Stochastics, 2021, 93(4): 625-645.
[8] Han Y C, Li Z, Liu C Y. Option pricing under the fractional stochastic volatility model. ANZIAM J., 2021, 63(2):123-142.
[9] Han Y C, Zhao L X. A closed-form pricing formula for variance swaps under MRG-Vasicek model. Comput. Appl. Math., 2019, 38(3):142.
[10] Zhang J C, Lu X P, Han Y C. Pricing perpetual timer option under the stochastic volatility model of Hull-White. ANZIAM J., 2017, 58(3-4):406-416.
动力系统与随机偏微分方程研究方向:
[1]Deng X Y, Han Y C, Strong law of large numbers and central limit theorem for stochastic lattice differential equations. Discrete Contin. Dyn. Syst. Ser. B, 2025, 30(4):1121-1146.
[2]Han Y C, Wu G Y. Feynman-Kac formula for parabolic Anderson model in Gaussian potential and fractional white noise. J. Math. Phys., 2024, 65(2):021502.
[3]Han Y C, Wu G Y. Hölder continuity of stochastic heat equation with rough Gaussian noise. Statistics & Probability Letters, 2024,210:110119
[4]Zhou X P, Jiang X M, Li Y, Han Y C. Periodic solutions of stochastic functional differential equations with jumps via viability. J. Dynam. Differential Equations, 2022,34(3):2429-2463.
[5]Chen F, Han Y C, Li Y, Yang X. Periodic solutions of Fokker–Planck equations. J. Differential Equations, 2017, 263(1): 285-298.
[6]Chen F, Han Y C. Existence of time-periodic weak solutions to the stochastic Navier-Stokes equations around a moving body. J. Math. Phys., 2013, 54(12):123101.
[7] Han Y C, Zhang L W. Mild solution to parabolic Anderson model in Gaussian and Poisson potential. J. Math. Phys., 2013, 54(10):103503.
[8]Han Y C, Li Y, Yi Y F. Invariant tori in Hamiltonian systems with high order proper degeneracy. Ann. Henri Poincaré, 2010, 10(8):1419-1436.
[9]Han Y C, Li Y, Yi Y F. Degenerate lower-dimensional tori in Hamiltonian systems. J. Differential Equations, 2006, 227(2):670-691.
[10]Han Y C, Li Y. Arnold's theorem on properly degenerate systems with the Rüssmann nondegeneracy. Sci. China Ser. A, 2005, 48(12):1656-1669.
随机过程的统计推断研究方向:
[1]Han Y C, Zhang D W. Nonlinear least squares estimator for generalized diffusion processes with reflecting barriers. Stochastics, 2025, 97(1):1-20.
[2]Han Y C, Zhang D W. Nadaraya-Watson estimators for reflected stochastic processes. Acta Math. Sci. Ser. B (Engl. Ed.), 2024, 44(1):143-160.
[3]Han Y C, Hu Y Z, Zhang D W. Modified least squares estimators for Ornstein-Uhlenbeck processes from low-frequency observations. Appl. Math. Lett., 2024, 156:109143.
[4]Han Y C, Zhang D W. Nadaraya-Watson estimators for stochastic differential equations driven by fractional Brownian motion. Stoch. Models, 2024, 40(3):502-517.
[5]Han Y C, Zhang D W. Local linear estimator for fractional diffusions[J]. Stoch. Dyn., 2024, 24(3):2450020.
[6]Han Y C, Zhang D W. Modified trajectory fitting estimators for multi-regime threshold Ornstein-Uhlenbeck processes. Stat, 2023, 12(e620):1-11.