Abstract Time-varying behavioral features and non-linear dependence are widely observed in big data and challenge the operating systems and processes of risk management in financial services. In order to improve the operational accuracy of risk measures and incorporate customer behavior analytics, we propose a Bayesian approach to efficiently estimate the multivariate risk measures in a dynamic framework. The proposed method can carry the prior information into the Bayesian analysis and fully describe the risk measures’ behavior after utilizing the Cornish–Fisher (CF) approximation with Markov Chain Monte Carlo (MCMC) sampling. Therefore, the operating systems and processes of risk management can be well performed either based on the first four conditional moments of the underlying model employed to consider some specific behavioral features (e.g., the time-varying conditional multivariate skewness) or the characteristics extracted from the big data. We conduct a simulation study to distinguish the applications of CF approximation and MCMC sampling after comparing them with the classic likelihood based method. We then provide a robust procedure for empirical investigation by using the real data of U.S. DJIA stocks. Both simulation and empirical results confirm that the Bayesian method can significantly improve the operations of risk management.

中文翻译：

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使用贝叶斯方法进行行为数据驱动的金融服务风险管理分析

摘要 大数据中广泛存在的时变行为特征和非线性依赖性对金融服务中的风险管理操作系统和流程提出了挑战。为了提高风险度量的操作准确性并结合客户行为分析，我们提出了一种贝叶斯方法，以在动态框架中有效地估计多变量风险度量。所提出的方法可以将先验信息带入贝叶斯分析中，并在利用马尔可夫链蒙特卡罗（MCMC）抽样的康沃尔-费舍尔（CF）近似后充分描述风险度量的行为。因此，基于用于考虑某些特定行为特征的基础模型的前四个条件矩，可以很好地执行风险管理的操作系统和流程（例如，时变条件多元偏度）或从大数据中提取的特征。在与经典的基于似然的方法进行比较后，我们进行了模拟研究，以区分 CF 近似和 MCMC 采样的应用。然后，我们通过使用美国 DJIA 股票的真实数据提供了一个稳健的实证调查程序。模拟和实证结果都证实，贝叶斯方法可以显着改善风险管理的运作。然后，我们通过使用美国 DJIA 股票的真实数据提供了一个稳健的实证调查程序。模拟和实证结果都证实，贝叶斯方法可以显着改善风险管理的运作。然后，我们通过使用美国 DJIA 股票的真实数据提供了一个稳健的实证调查程序。模拟和实证结果都证实，贝叶斯方法可以显着改善风险管理的运作。