A Multi-Curve Random Field LIBOR Market Model
演讲者
Tao Wu
时间
2024年12月23日 15:20 至 16:20
地点
A3-2-303
线上
Zoom 230 432 7880
(BIMSA)
摘要
A multi-curve random field LIBOR market model is proposed by extending the LIBOR market model (LMM) with uncertainty modelled by a random field to the multi-curve framework, where the forward LIBOR curve for projecting future cash flows and the curve for discounting are modelled distinctively but jointly. The multi-curve methodology is introduced recently in the literature to account for the increased basis among closely related interest rates since the 2007-2009 credit crisis. Closed-form formulas for pricing caplets and swaptions are derived. Then the multi-curve random field LIBOR market model is integrated with the local and stochastic volatility models (lognormal-mixture, SABR, Wu and Zhang (2006)) to capture the implied volatility skew/smile. Finally, we estimate various models from market data. Empirical results show that for both in-sample and out-of-sample pricing, the random field LIBOR Market Model outperforms the Brownian motion LIBOR Market Model. Moreover, their corresponding multi-curve variations outperform their single-curve counter-parts respectively.
演讲者介绍
Tao Wu is an associate professor of finance at the Stuart School of Business at Illinois Institute of Technology. He has been awarded the Irwin/McGraw-Hill Distinguished Paper in Finance Prize, Best Paper awards at the Financial Management Association Conferences and the Asia-Pacific Association for Derivatives Conference, Van Buren and Van Amringe Prizes in Mathematics, Josephine de Karman Fellowship, Professor Harriss Scholarship in Economics, and an I. I. Rabi Scholar. He has presented his research at the American Finance Association annual meetings, The Wharton School, Stanford University, University of Illinois, Rutgers University, the Federal Reserve Board, the FDIC, and the OCC. His research has been funded by the Arditti Center for Risk Management and the Chicago Mercantile Exchange. He has published extensively in leading academic finance journals. He received his Ph.D. in finance from The Wharton School at the University of Pennsylvania and B.A. in economics from Columbia University. Prior to graduate school, he traded derivatives for a major investment bank in New York.