针对金融高频数而开发的极差波动估计量因能更精确地度量波动率而备受关注. 根据方差有效性结合数值模拟, 推导出了已实现极差多幂次变差族中最优的波动估计量, 并依据无偏性和方差有效性给出了相应的加权估计量. 同时将这些估计量与已实现GRACH模型相结合, 并对模型进行扩展. 实证表明已实现极差四幂次变差是已实 现极差多幂次变差族中最优的波动估计量, 加权的已实现极差四幂次变差能有效消除日历效应的影响, 扩展的已实现GRACH模型在拟合和预测效果上明显优于传统的EGARCH模型.
Abstract
Range-based volatility aiming at financial high-frequency data has attracted more and more attention for its more accurate estimation of financial asset's volatility. The paper derives the optimal volatility estimator in the family of realized range-based multipower variation, according to variance efficiency with numerical simulation and it also gives its weighted estimator, according to unbiasedness and variance efficiency. Meanwhile, the paper expands the realized GARCH model under the condition that the realized GARCH model is combined with these estimators. The empirical analyses show that realized range-based quadpower variation is the optimal volatility estimator in the family of realized range-based multipower variation, the weighted realized range-based quadpower variation does get rid of the influence of calendar effect and the expanded realized GARCH models outperform traditional EGARCH model in fit and forcasting.
关键词
已实现极差多幂次变差 /
日历效应 /
已实现GARCH /
预期不足
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Key words
range-based multipower variation /
calendar effect /
realized-GARCH model /
expected shortfall
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中图分类号：
F830
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参考文献
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脚注
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基金
国家自然科学基金(71171056)
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