全奖博士丨曼彻斯特大学招收金融计量经济学模型算法开发方向全奖博士

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项目简介 In modern statistical applications, many complicated models have two common features. First the likelihood functions are often difficult to evaluate; second the model is generative. In particular, financial time series data pose the following challenges. First, when latent stochastic dynamics are considered, e.g. volatilities and regime switching, the likelihood is intractable. Second, in the big data era, the more sophisticated model is required for high-frequency data and their microstructure. The class of simulation-based methods is often used for statistical inference of intractable likelihood models by using model simulations. The inference is usually conducted under the Bayesian framework, providing uncertainty quantifications for both parameter estimation and prediction. It has seen successful applications and become increasingly popular in a wide range of areas, including population genetics, ecology, astronomy, etc. This project aims to develop new simulation-base ………………………………