Financial Systems Research
Financial engineering concerns the application of analytical, statistical, and computational methods to solve problems in financial economics. It is a multidisciplinary field that draws on tools from applied mathematics, computer science, statistics, and economic theory. Faculty at UC Berkeley IEOR conduct various research projects in credit risk, real options, high-frequency trading, and portfolio management. These research activities have been broadly supported by the National Science Foundation, National Security Agency, and various industry partners including Bloomberg and the NASDAQ OMX educational group. The research team attracts the best quality and highly motivated students, who go through rigorous and deep analytical training in mathematics and statistics, and develop proficiency in hand-on skills such as programming. Over the last decades, they have been aggressively recruited by the top investment banks around the globe as well as high-tech firms including Google and Facebook.
Zeyu Zheng, Xiangyu Yang, Yanfeng Wu, and Jian-Qiang Hu. Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models. Probability in the Engineering and Informational Sciences. https://doi.org/10.1017/S0269964820000315.
X. Guo, A. de Larrard and Z. Ruan. Optimal placement in a limit order book, an analytical approach, Mathematics and Financial Economics, DOI: 10.1007/s11579-016- 0177-5, 2016.
X. Guo and M. Zervos. Optimal execution with multiplicative price impact, SIAM Journal on Financial Mathematics, 6(1), 281-306, 2015.
Ilan Adler, Sushil Verma (2013), A direct reduction of PPAD Lemke-verified linear complementarity problems to bimatrix games, arXiv:1302.0067.
Ilan Adler (2012), The equivalence of linear programs and zero-sum games, International Journal of Game Theory.
X. Guo, P. Kaminsky, P. Tomecek, and M. K. Yuen. Optimal spot market inventory strategy in the presence of cost and price risk, Mathematical Methods for Operations Research,73:109-137, 2011.