标题： 有效建模与预测基于范围的波动性模型及其应用 显示英文标题

标题： Efficient Modelling & Forecasting with range based volatility models and application

摘要： 本文考虑了一种使用组合估计函数（CEF）拟合CARR模型的替代方法，并展示了其在经济学和定量金融应用中的实用性。推导了相应新估计量的相关信息矩阵以计算标准误差。通过模拟研究展示了CEF相对于另外两个竞争方法：线性估计函数（LEF）和最大似然（ML）的优势。结果显示，当误差分布被错误指定时，CEF估计量比LEF和ML估计量更有效。利用来自金融经济学的真实数据集，我们展示了CEF方法在实际应用中的实用性和适用性，并报告了可靠的预测值以最小化决策过程中的风险。 显示英文摘要

摘要： This paper considers an alternative method for fitting CARR models using combined estimating functions (CEF) by showing its usefulness in applications in economics and quantitative finance. The associated information matrix for corresponding new estimates is derived to calculate the standard errors. A simulation study is carried out to demonstrate its superiority relative to other two competitors: linear estimating functions (LEF) and the maximum likelihood (ML). Results show that CEF estimates are more efficient than LEF and ML estimates when the error distribution is mis-specified. Taking a real data set from financial economics, we illustrate the usefulness and applicability of the CEF method in practice and report reliable forecast values to minimize the risk in the decision making process.