论文标题
使用基于特征函数的线性状态空间表示估算选项定价模型
Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation
论文作者
论文摘要
我们为参数选项定价模型开发了一种新颖的过滤和估计程序，该模型由一般的仿射跳转延伸驱动。我们的过程基于条件对数特性函数的选项图像，无模型表示的比较，并在模型的状态向量中功能上呈现。我们正式得出相关的线性状态空间表示，并建立相应测量误差的渐近特性。状态空间表示允许我们使用适当修改的Kalman滤波技术来了解模型参数的潜在状态向量和准最大可能性估计器，从而带来重要的计算优势。我们在蒙特卡洛模拟中分析了我们程序的有限样本行为。在两个案例研究中说明了我们程序的适用性，分析了标准普尔500期期权价格的价格以及捕获Covid-19-19的复制和经济政策不确定性的外源性状态变量的影响。
We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally affine in the model's state vector. We formally derive an associated linear state space representation and establish the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, which brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.
