英文标题：
《A Splitting Strategy for the Calibration of Jump-Diffusion Models》
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作者：
Vinicius Albani and Jorge Zubelli
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最新提交年份：
2018
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英文摘要：
We present a detailed analysis and implementation of a splitting strategy to identify simultaneously the local-volatility surface and the jump-size distribution from quoted European prices. The underlying model consists of a jump-diffusion driven asset with time and price dependent volatility. Our approach uses a forward Dupire-type partial-integro-differential equations for the option prices to produce a parameter-to-solution map. The ill-posed inverse problem for such map is then solved by means of a Tikhonov-type convex regularization. The proofs of convergence and stability of the algorithm are provided together with numerical examples that substantiate the robustness of the method both for synthetic and real data.
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中文摘要：
我们对分割策略进行了详细的分析和实施，以从欧洲报价中同时确定局部波动面和跳跃大小分布。基础模型由跳跃扩散驱动的资产组成，其波动率与时间和价格相关。我们的方法使用期权价格的前向Dupire型偏积分微分方程来生成参数到解的映射。然后利用Tikhonov型凸正则化方法求解该映射的不适定反问题。文中给出了算法的收敛性和稳定性证明，并通过数值算例验证了该方法对合成数据和真实数据的鲁棒性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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