A Jump-Diffusion Model for Option Pricing
Management Science
Online option price forecasting by using unscented Kalman filters and support vector machines
Expert Systems with Applications: An International Journal
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models
Expert Systems with Applications: An International Journal
Option valuation based on the neural regression model
Expert Systems with Applications: An International Journal
Equity warrants pricing model under Fractional Brownian motion and an empirical study
Expert Systems with Applications: An International Journal
A new application of fuzzy set theory to the Black-Scholes option pricing model
Expert Systems with Applications: An International Journal
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
Transductive Bayesian regression via manifold learning of prior data structure
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
We investigate a parametric method for calibrating European option pricing using the state-of-art exponential Levy models. We propose a derivative-free calibration method constrained by four observable statistical moments (mean, variance, skewness and kurtosis) from underlying time series to conquer the ill-posed inverse problem and to incorporate priors on observable statistical moments. We present a numerical implementation scheme for calibrating the exponential Levy models and show that it can resolve the instability of the inverse problems empirically and can produce good calibration results. In particular, we apply our approach to real market data sets of S&P 500 call options with significantly better performance.