标题： 随机波动率模型基于时间变换变换的推断 显示英文标题

标题： Inference for stochastic volatility models using time change transformations

摘要： 我们通过马尔可夫链蒙特卡洛（MCMC）方法解决由扩散驱动的随机波动率模型中的参数估计问题。 为了避免退化问题，我们引入了一种创新的重参数化方法，该方法通过作用于扩散时间尺度上的变换来定义。此外，还提出了一种新的MCMC方案，以克服时间变化变换固有的困难。 该算法易于实现，并适用于具有随机波动率的模型。 该方法通过基于模拟的实验进行测试，并在美国国库券利率数据上进行了说明。 显示英文摘要

摘要： We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.