Pricing Average and Spread Options Under Local-Stochastic Volatility Jump-Diffusion Models
提出一种新近似公式，用于在局部随机波动跳跃扩散模型中对多维离散监测平均期权定价，并通过数值实验验证了该方法的有效性。
This paper presents a new approximation formula for pricing multi-dimensional discretely monitored average options in a local-stochastic volatility (LSV) model with jumps by applying an asymptotic expansion technique. Moreover, it provides a justification of the approximation method with some asymptotic error estimates for general payoff functions. Particularly, our model includes local volatility functions and jump components in the underlying asset price as well as its volatility processes. To the best of our knowledge, the proposed approximation is the first one which achieves analytic approximations for the average option prices in this environment. In numerical experiments, by employing several models, we provide approximate prices for the listed average and calendar spread options on the WTI futures based on the parameters through calibration to the listed (plain-vanilla) futures options prices. Then, we compare those with the CME settlement prices, which confirms the validity of the method. Moreover, we show that the LSV with jumps model is able to replicate consistently and precisely listed futures option, calendar spread option and average option prices with common parameters.