结构突变条件下农产品期货市场波动率的预测

在检验农产品期货已实现波动率序列的结构突变等特征基础上,通过构造不同估计窗口大小的ARFIMAX-FIGARCH模型及其线性和非线性组合预测模型来预测农产品期货市场的已实现波动率,并采用基于自助法的MCS检验评价和比较各类预测模型的预测性能。研究结果表明:农产品期货的已实现波动率序列都表现出结构突变特征、不对称性和双长记忆性,并且结构突变点都与一连串的宏观面、政策面重大事件冲击有关;对基于不同估计窗口大小的ARFIMAX-FIGARCH模型所得的单项预测值进行时变加权组合通常能够提供更准确的波动率预测值,并且基于NKR的非参数组合预测模型和基于NRLS和SIC的线性组合预测模型是在结构突变条件下预测农产品期货市场波动率尤其有效的方法。

Volatility Projection for Agricultural Commodity Futures under Structural Breaks

This study explore the possibility of structural breaks in daily realized volatility series of agricultural commodity futures, and conduct an out-of-sample forecast to explore the effects of structural break on the performance of ARFIMAX-FIGARCH models for the realized volatility forecast, concentrating on procedures that utilize a variety of estimation window sizes designed to accommodate the potential structural breaks. The results indicate that the realized volatility of agricultural commodity futures exhibits the properties of structural breaks, asymmetry, and double long memory. In addition, combination forecasts with time varying weights across individual forecast models estimated with different estimation windows performs well, and the nonlinear combination forecasts with weights chosen based on a nonparametric kernel regression and the linear combination forecasts with weights chosen based on non-negative restricted least squares and Schwarz Information Criterion appears to be the most effective methods for forecasting the realized volatility of agricultural commodity futures under structural breaks.