英文文献：ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models-SV的ABC:随机波动跳扩散模型中的有限信息似然推断
英文文献作者：Michael Creel,Dennis Kristensen
英文文献摘要：
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. Despite their computational simplicity, we find that estimators and filters perform well in practice and lead to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the S&P 500 equity index. We find evidence of the presence of a dynamic jump rate and in favor of a structural break in parameters at the time of the recent financial crisis. We find evidence that possible measurement error in log price is small and has little effect on parameter estimates. Smoothing shows that, recently, volatility and the jump rate have returned to the low levels of 2004-2006.

我们开发了新的方法来估计和过滤连续时间模型随机波动和跳跃使用所谓的近似贝叶斯计算，建立概率基于有限的信息。相对于基于概率的标准版本，所提出的估计器和过滤器在计算上具有吸引力，因为它们依赖于低维辅助统计，从而避免了高维积分的计算。尽管它们的计算简单，我们发现估计器和过滤器在实践中表现良好，并导致模型参数和潜在变量的精确估计。我们将展示这些方法如何结合每日内信息来改进估计和过滤。特别是，已实现的波动度量的可用性帮助我们学习参数和潜在状态。将该方法应用于标普500股票指数动态的随机弹性波动模型的估计。在最近的金融危机中，我们发现了动态跳跃率的存在和有利于参数的结构性断裂的证据。我们发现，日志价格中可能的测量误差很小，对参数估计的影响很小。平滑显示，最近，波动性和跳跃率已回到2004-2006年的低水平。