Fixed-point iterative algorithm for SVI model

Autor:	Yang, Shuzhen, Zhang, Wenqing
Rok vydání:	2023
Předmět:	Quantitative Finance - Mathematical Finance
Druh dokumentu:	Working Paper
Popis:	The stochastic volatility inspired (SVI) model is widely used to fit the implied variance smile. Presently, most optimizer algorithms for the SVI model have a strong dependence on the input starting point. In this study, we develop an efficient iterative algorithm for the SVI model based on a fixed-point and least-square optimizer. Furthermore, we present the convergence results in certain situations for this novel iterative algorithm. Compared with the quasi-explicit SVI method, we demonstrate the advantages of the fixed-point iterative algorithm using simulation and market data. Comment: 32 pages, 11 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.07830 Zobrazit plný text záznamu View this record from Arxiv