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Thursday, January 18, 2018 at 11:00am
Asymptotic analyses of financial problems have a wide spectrum of applications ranging from the statistical inference of stochastic processes based on high-frequency data to near-expiration expansions of options prices and implied volatilities, and Monte Carlo based methods for path-dependent options. These methods are especially useful to study complex models with jumps and stochastic volatility due to the lack of tractable formulas and efficient numerical procedures. In this talk, I will discuss some recent advances in the area and illustrate their broad relevance in several contexts.
José Figueroa-López is a full professor of mathematics at Washington University in St. Louis. Formerly he was associate professor of statistics at Purdue University, where he served as associate director of the Computational Finance Program. Professor Figueroa’s ongoing research includes short-time asymptotics of jump-diffusion models, diffusion limits of limit order book models, optimal limit order placement problems, and optimal tuning of high-frequency based econometric methods. He was awarded the NSF career award in 2012 and currently has two active NSF grants on the interplay of finance, statistics, and probability. He is associate editor of the SIAM Journal on Financial Mathematics (SIFIN) and a former associate editor of Electronic Journal of Statistics.