Risk Magazine Cutting Edge Article | Multi-curve Cheyette-style models with lower bounds on tenor basis spreads
This article presents a general multi-curve Cheyette-style model that allows precise control over tenor basis spreads. The specification was proposed by Grbac and Runggaldier, but a solution for the no-arbitrage drift function has remained elusive. Drs. Michael Konikov and Andrew McClelland of Numerix recover the drift function via an ansatz and proceed to fully develop the model, providing an example with a level-dependent volatility function to secure lower bounds on spreads.
Complete the form to download this Risk.net research paper, “Multi-curve Cheyette-style models with lower bounds on tenor basis spreads”.
Authors: Dr. Michael Konikov, Andrew McClelland, Ph.D.
This article presents a general multi-curve Cheyette-style model that allows precise control over tenor basis spreads. The specification was proposed by Grbac and Runggaldier, but a solution for the no-arbitrage drift function has remained elusive. Drs. Michael Konikov and Andrew McClelland of Numerix recover the drift function via an ansatz and proceed to fully develop the model, providing an example with a level-dependent volatility function to secure lower bounds on spreads.
Complete the form to download this Risk.net research paper, “Multi-curve Cheyette-style models with lower bounds on tenor basis spreads”.
Authors: Dr. Michael Konikov, Andrew McClelland, Ph.D.
Authors
Dr. Michael Konikov
Dr. Michael Konikov is a Senior Vice President and Head of Quantitative Development at Numerix, where he manages a team responsible for the development and delivery of models in Numerix software. Previously, he worked at Citigroup, Barclays, and Bloomberg in quantitative research and desk quant roles. He completed his PhD in mathematical finance at the University of Maryland College Park, concentrating in particular on the application of pure jump processes to option pricing. Dr. Konikov's publications cover diverse asset classes ranging from equity to interest rates and credit.
Andrew McClelland, Ph.D.
Andrew McClelland’s quantitative research at Numerix focuses on XVA pricing and hedging, generating counterparty credit risk metrics for structured products, and estimating risk model parameters via time-series estimation. He earned his PhD in finance at the Queensland University of Technology for a thesis on financial econometrics. He considered markets exhibiting crash feedback, option pricing for such markets, and parameter estimation for such markets using particle filtering methods. Dr. McClelland’s work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.
