A new algorithm for calibrating local regime-switching models. (27th July 2020)

• Record Type: Journal Article
• Authors: He, Xin-Jiang; Zhu, Song-Ping
• Is Part Of: IMA journal of management mathematics. Volume 32:Number 2(2021)
• Page Start: 237
• Page End: 255
• Publication Date: 2020-07-27
• Subjects: local regime-switching model -- calibration -- empirical test -- risk management; Management -- Mathematical models -- Periodicals; Management science -- Mathematical models -- Periodicals; Business mathematics -- Periodicals; 650.01513
• DOI: 10.1093/imaman/dpaa012
• Languages: English
• ISSNs: 1471-678X

Abstract

Abstract: In quantitative finance practice, model calibration is a key challenge. This is especially so when a local regime-switching model needs to be calibrated because designing an efficient and reliable algorithm to obtain local volatility values as a function of underlying price and time is important for the model to be successfully used in practice. Therefore, this paper proposes a new algorithm for calibrating local regime-switching models with observed option prices available for a particular market that is suitable for this type of model. The newly proposed algorithm is tested with calibrations performed on synthetic as well as real market data. Our empirical test results indicate that the algorithm has great potential to be used in financial risk management.