摘要翻译：
大量的经验证明了Taylor提出的时间离散自回归随机波动率模型在描述金融资产对数收益方面的充分性。由于相应市场的不完全性质，使用这些模型为其基础资产定价和套期保值的或有产品是一项非平凡的工作。本文应用文献中的两种波动率估计技术，即Kalman滤波和分层似然方法，来实现各种定价和动态套期保值策略。我们的研究表明，F\'Ollmer，Schweizer和Sondermann提出的局部风险最小化方案在这种情况下特别适用，尤其是在货币期权中和在低套期保值频率下。
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英文标题：
《Hedging of time discrete auto-regressive stochastic volatility options》
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作者：
Joan del Castillo and Juan-Pablo Ortega
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
  Numerous empirical proofs indicate the adequacy of the time discrete auto-regressive stochastic volatility models introduced by Taylor in the description of the log-returns of financial assets. The pricing and hedging of contingent products that use these models for their underlying assets is a non-trivial exercise due to the incomplete nature of the corresponding market. In this paper we apply two volatility estimation techniques available in the literature for these models, namely Kalman filtering and the hierarchical-likelihood approach, in order to implement various pricing and dynamical hedging strategies. Our study shows that the local risk minimization scheme developed by F\"ollmer, Schweizer, and Sondermann is particularly appropriate in this setup, especially for at and in the money options or for low hedging frequencies.
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PDF链接：
https://arxiv.org/pdf/1110.6322