气温指数保险定价中的温度预测模型评价——基于保险人及被保险人视角

合理定价气温指数保险关系着保险人及被保险人共同利益,而温度预测是气温指数保险定价的关键,现有关于温度预测模型的选择只是单纯从预测精度考虑问题,评价标准过于单一。根据气温指数保险投保人多为广大农民的现实,基于保险人及被保险人视角,构建了更加客观的评价模型,并以长江中下游中稻气温指数保险合约为例,比较了时间序列模型和回归模型,结果表明,如果单纯从模型预测精度角度来看,两种模型基本相当;但若运用所构建的评价模型来分析,用时间序列模型预测气温,进而为合约定价,可使保险公司获得更高的保费收入,对被保险人来说,虽然支付了稍高的费用,但期末获得正收益的概率却大大提高了,因此时间序列模型较优。     

Combining estimating functions for volatility

Accurate estimates of volatility are needed in risk management. Generalized autoregressive conditional heteroscedastic (GARCH) models and random coefficient autoregressive (RCA) models have been used for volatility modelling. Following Heyde [1997. Quasi-likelihood and its Applications. Springer, New York], volatility estimates are obtained by combining two different estimating functions. It turns out that the combined estimating function for the parameter in autoregressive processes with GARCH errors and RCA models contains maximum information. The combination of the least squares (LS) estimating function and the least absolute deviation (LAD) estimating function with application to GARCH model error identification is discussed as an application.     

Buffered Autoregressive Models With Conditional Heteroscedasticity: An Application to Exchange Rates

This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2015 Li, G.D., Guan, B., Li, W.K., and Yu, P. L.H. (2015), “Hysteretic Autoregressive Time Series Models,” Biometrika, 102, 717–723.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), can capture the buffering phenomena of time series in both the conditional mean and variance. Thus, it provides us a new way to study the nonlinearity of time series. Compared with the existing AR-GARCH and threshold AR-GARCH models, an application to several exchange rates highlights the importance of the BAR-GARCH model.