Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH‑MIDAS model

This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)futures.We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models.Our empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type models.The results also indicate that EPU has noticeable effect on the volatility of EUA futures.Specifically,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index.Robustness checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures prices.Additionally,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.

SKEWNESS OF RETURN DISTRIBUTION AND COEFFICIENT OF RISK PREMIUM

The skewness of the return distribution is one of the important features of the security price.In this paper,the authors try to explore the relationship between the skewness and the coefficient ofrisk premium.The coefficient of the risk premium is estimated by a GARCH-M model,and the robustmeasurement of skewness is calculated by Groeneveld-Meeden method.The empirical evidences forthe composite indexes from 33 securities markets in the world indicate that the risk compensationrequirement in the market where the return distribution is positively skewed is virtually zero,andthe risk compensation requirement is positive in a significant level in the market where the returndistribution is negative skewed.Moreover,the skewness is negatively correlated with the coefficient ofthe risk premium.

DESIGNING A HYBRID INTELLIGENT MINING SYSTEM FOR CREDIT RISK EVALUATION

在这研究，集成不平的集合理论和支持向量机器的一个新奇混合聪明的采矿系统被开发高效地为信用风险评估和分析从原来的信息桌子提取协会规则。在建议混合聪明的系统，支持向量机器被用作一个工具提取典型特征并且过滤它的噪音，它与不平的集合仅仅为支持向量机器被用作一个预处理器的以前的研究不同。如此的一条途径能减少信息桌子并且由不平的集合从减少的信息桌子产生最后的知识。因此，建议混合聪明的系统克服从一个训练支持向量机器分类器提取规则的 dificulty 并且拥有为 rough-set-based 途径正在缺乏的坚韧性。另外，建议混合聪明的系统的有效性与二真实世界的信用数据集被说明。