Variation-Based Tests for Volatility Misspecification
Kay Giesecke
Founder, Chairman and Chief Scientist, Professor at Stanford University
Alex Papanicolaou
Co-Founder and CTO
We provide a simple and easy to use goodness-of-fit test for the misspecification of the volatility function in diffusion models. The test uses power variations constructed as functionals of discretely observed diffusion processes. We introduce an orthogonality condition which stabilizes the limit law in the presence of parameter estimation and avoids the necessity for a bootstrap procedure that reduces performance and leads to complications associated with the structure of the diffusion process. The test has good finite sample performance as we demonstrate in numerical simulations.
This work was published in the Journal of Econometrics, volume 191(1), pages 217-2310, 2016.
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About the Speaker
Kay Giesecke
Founder, Chairman and Chief Scientist, Professor at Stanford University
Kay Giesecke is the Founder, Chairman and Chief Scientist at Infima. He is also Professor of Management Science & Engineering at Stanford University, the director of the Advanced Financial Technologies Laboratory, and the director of the Mathematical and Computational Finance Program. Kay serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society.
Kay is a financial technologist interested in solving the challenging modeling, statistical, and computational problems arising in fixed-income and credit markets. Together with his students at Stanford, Kay has pioneered the core elements of the deep learning and computational technologies underpinning Infima’s solutions.
Kay’s research has won several awards, including the JP Morgan AI Faculty Research Award (2019) and the Fama/DFA Prize (2011), and has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, Swiss Re, American Express, Moody's,and several other organizations.
Kay has advised several financial technology startups and has been a consultant to banks,investment and risk management firms, governmental agencies, and supranational organizations.
Alex Papanicolaou
Co-Founder and CTO
Alex Papanicolaou co-founded infima and serves as CTO. His specialities include data science, machine learning, computational algorithms, statistics, software development, and more. He specifically leads development of infima's suite of forecasting models and computational algorithms for fast prediction delivery.
Prior to infima, he was a Senior Data Scientist at Integral Development Corporation working on challenging data intensive problems in foreign exchange markets to deliver analytics for trade execution. He developed analyses for sell-side brokers to assess order flow quality, models to support improving execution quality in the matching engine, and a novel use of computer vision methods to detect adversarial actions taken against buy-side traders.
He also served as a postdoctoral researcher and instructor at UC Berkeley in the Consortium for Data Analytics in Risk where he developed and taught a course on sports analytics for aspiring undergraduate data science students. He received his PhD in computational mathematics from Stanford University in 2013 for work on statistical tests for dynamic stochastic processes used in financial mathematics.
