Semiparametric Inference in a GARCH-in-Mean Model
Semiparametric Inference in a GARCH-in-Mean Model
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DOI：
10.2139/ssrn.1262230
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发表时间：
2008-09
期刊：
影响因子：
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通讯作者：
B. Christensen;C. Dahl;E. Iglesias
中科院分区：
文献类型：
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作者：
B. Christensen;C. Dahl;E. Iglesias
对具有一般非参数风险收益权衡和GARCH型潜在波动率的经验资产定价模型，给出了一个新的半参数估计。基于轮廓似然方法，它不依赖于条件均值函数的任何初始参数估计，并且在规定的条件下是一致的，渐近正态的和有效的，即，它达到了半参数下界。一个抽样实验提供了与参数方法和Conrad和Mammen（2008）的参数初始估计的迭代半参数方法的有限样本比较。每日股票市场收益的应用表明，风险收益关系确实是非线性的。
A new semiparametric estimator for an empirical asset pricing model with general nonparametric risk-return tradeoff and GARCH-type underlying volatility is introduced. Based on the profile likelihood approach, it does not rely on any initial parametric estimator of the conditional mean function, and it is under stated conditions consistent, asymptotically normal, and efficient, i.e., it achieves the semiparametric lower bound. A sampling experiment provides finite sample comparisons with the parametric approach and the iterative semiparametric approach with parametric initial estimate of Conrad and Mammen (2008). An application to daily stock market returns suggests that the risk-return relation is indeed nonlinear.