A Stochastic Volatility Model With a General Leverage Specification
提出一种新的随机波动模型，允许测量方程和对数波动方程在不同时间滞后下具有一般相关结构，从而更好地刻画金融时间序列中的杠杆效应及其传播，并嵌套其他非对称波动模型，可用于检验和诊断。
We introduce a new stochastic volatility model that postulates a general correlation structure between the shocks of the measurement and log volatility equations at different temporal lags. The resulting specification is able to better characterize the leverage effect and propagation in financial time series. Furthermore, it nests other asymmetric volatility models and can be used for testing and diagnostics. We derive the simulated maximum likelihood and quasi maximum likelihood estimators and investigate their finite sample performance in a simulation study. An empirical illustration shows that the postulated correlation structure improves the fit of the leverage propagation and leads to more precise volatility predictions.