Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model
提出一种新的贝叶斯半参数模型，用于捕捉每日股票指数收益的厚尾、非对称、波动聚集和杠杆效应等典型特征，并应用于S&P 500、FTSE 100和EUROSTOXX 50指数，与GARCH等模型比较。
This article introduces a new family of Bayesian semiparametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely, heavy tails, asymmetry, volatility clustering, and the “leverage effect.” A Bayesian nonparametric prior is used to generate random density functions that are unimodal and asymmetric. Volatility is modeled parametrically. The new model is applied to the daily returns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared with GARCH, stochastic volatility, and other Bayesian semiparametric models.