中文摘要：

面向文化资产证券化定价问题,构造嵌入Gamma过程的Bass随机扩散模型（G-Bass模型）,以预测文化产品的市场接受度,进而解决由于文化资产未来收益的强烈不确定性而引起的资产定价困难。论文首先给出了G-Bass随机扩散模型形式;然后,根据模型给出的资产收益的价格形式,利用新增资产收益信息,使用贝叶斯参数推断方法,对模型的参数进行更新;借助马尔科夫蒙特卡罗（MCMC）方法解决积分过程中的多重积分问题,最终可以实现资产的动态定价。本文采用中国大陆电影票房数据对该方法进行了实证效果分析,结果显示该方法具有较好的预测效果,具有可行性和适用性。本文为文化创意资产的定价提供一种新思路。

英文摘要：

The cultural-asset securitization is an important financial innovation direction. Pricing a cultural-asset is challenging because of strong uncertainty of its future gain, which is closely related to market acceptance of a cultural product. The prediction of market acceptance of a cultural product is researched, which is the key of a culture-asset pricing for cultural-asset securitization. The Bass Stochastic Diffusion model with Gamma process （G-Bass model） is used to predict the market acceptance of culture products. The parameters are firstly estimated by the method of maximum likelihood estimation, and then updated by Bayesian parametric inference method based on the new information. In this process, MCMC method is applied to solve multiple integration problems. As a result, the dynamic pricing of assets is realized. Based on the data of ticket-office taking in the mainland, the empirical research results verify that the method, a novel way for pricing of cultural- assets securitization, is feasible and effective. These parameters formulate market volatility, market acceptance＇s latent capacity and tendency. In the empirical research, initial and updated parameters are estimated based on the data of 146 films＇ tickets-taking in the mainland to realize the relevant asset pricing after the data are screened. All the steps are implemented as follows： （1） The parameters of a film＇s G-Bass model are estimated based on the time series of films＇ tickets-taking by maximum likelihood estimation; （2） The regression is given between the information of the film＇s character and these parameters based on historical film data; （3）The initial parameter vector of a upcoming film, denoted as 0, is estimated based on the film＇s character information; （4） The parameter vector is updated by Bayesian parametric inference method based on the new information in the market. The MCMC method is applied to solve the multiple integration problems in this process. It is showed that the G-Bass model can