  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.
