王小群 - 清华大学 - 数学科学系

个人简介

1995年获俄罗斯国立圣彼得堡大学博士学位。清华大学数学科学系长聘教师，国家杰出青年基金获得者，教育部长江学者特聘教授。担任或曾任全国应用统计专业学位研究生教育指导委员会委员，中国工业与应用数学学会秘书长、常务理事，中国运筹学会金融工程和风险管理分会副理事长。工作履历 1997年起在清华大学数学科学系任教，历任讲师、副教授、教授多次访问香港浸会大学、澳大利亚 The University of New South Wales, 加拿大 University of Waterloo 所授课程概率论（1）(本科生) 概率论（2）（研究生）概率论与数理统计（本科生）随机数学（本科生）金融数学（研究生）计算金融学（研究生）蒙特卡罗方法（研究生）统计案例与实务（研究生）奖励与荣誉清华大学学术新人奖获得者（2004）国家杰出青年基金获得者（2009）教育部长江学者特聘教授（2011）

研究领域

金融数学、计算金融学、统计计算与数据科学、随机模拟与确定性模拟方法、计算复杂性理论

近期论文

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Zhijian He and Xiaoqun Wang. Convergence analysis of quasi-Monte Carlo sampling for quantile and expected shortfall. Mathematics of Computation, Vol. 90, No. 327, 303-319, 2021. Chaojun Zhang, Xiaoqun Wang, Zhijian He. Efficient importance sampling in quasi-Monte Carlo methods for computational finance. SIAM Journal on Scientific Computing,Vol. 43, No.1, B1-B29, 2021. Chengfeng Weng, Xiaoqun Wang and Zhijian He. Efficient computation of option prices and Greeks by quasi-Monte Carlo method with smoothing and dimension reduction. SIAM Journal on Scientific Computing, Vol. 39, No. 2, B298-B322, 2017. Xiaoqun Wang. Handling discontinuities in financial engineering: A good path simulation approach. Operations Research, Vol. 64, No. 2, 297-314, 2016. Zhijian He and Xiaoqun Wang. On the convergence rate of randomized quasi-Monte Carlo f for discontinuous functions, SIAM Journal on Numerical Analysis, Vol. 53, No. 5, 2488-2503, 2015. Zhijian He, Xiaoqun Wang, Good path generation methods in quasi-Monte Carlo for pricing financial derivatives, SIAM Journal on Scientific Computing, 2014，Vol. 36, No. 2, pp. B171-B197, 2014. Xiaoqun Wang and K. S. Tan. Pricing and hedging with discontinuous functions: quasi-Monte Carlo Methods and dimension reduction. Management Science, Vol. 59, No. 2, 376-389, 2013. Xiaoqun Wang. Enhancing quasi-Monte Carlo by exploiting additive approximation for problems in finance. SIAM Journal on Scientific Computing, Vol.34, No.1, A283-A308, 2012. Xiaoqun Wang and I. H. Sloan. Quasi-Monte Carlo methods in financial engineering: an equivalent principle and dimension reduction. Operations Research, Vol. 59, No. 1, 80-95, 2011. Xiaoqun Wang. Dimension reduction techniques in quasi-Monte Carlo methods for option pricing. INFORMS Journal on Computing, Vol. 21, No.3, 488-504, 2009. Xiaoqun Wang. Constructing robust lattice rules for computational finance. SIAM Journal on Scientific Computing, Vol. 29, No. 2, 598 - 621, 2007. Xiaoqun Wang. On the effects of dimension reduction techniques on some high-dimensional problems in finance. Operations Research, Vol.54, No.6, 1063-1078, 2006. Xiaoqun Wang and I. H. Sloan. Efficient weighted lattice rules with applications to finance. SIAM Journal on Scientific Computing, Vol. 28, No.2, 728-750, 2006. Dick, J., I. H. Sloan, Xiaoqun Wang and H. Wozniakowski. Good lattice rules in weighted spaces with general weights. Numerische Mathematik, Vol. 103, No. 1, 63-97, 2006. Xiaoqun Wang and I. H. Sloan. Why are high-dimensional finance problems often of low effective dimension. SIAM Journal on Scientific Computing, Vol. 27, No.1, 159-183, 2005. Xiaoqun Wang, I. H. Sloan and J. Dick. On Korobov lattice rules in weighted spaces. SIAM Journal on Numerical Analysis, Vol. 42, No. 4, 1760-1779, 2004. Xiaoqun Wang. Strong tractability of multivariate integration using quasi-Monte Carlo algorithms. Mathematics of Computation, 2003, Vol.72, No. 242, 823-838, 2003. Xiaoqun Wang and Kai-Tai Fang. The effective dimensions and quasi-Monte Carlo integration. Journal of Complexity, Vol. 19, No. 2, 101-124, 2003. Hickernell, F. J. and Xiaoqun Wang. The error bounds and tractability of quasi-Monte Carlo algorithms in infinite dimension. Mathematics of Computation, Vol. 71, No. 240, 1641-1661, 2002.