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分类依据与纠错

AI 根据所提供的论文材料归类:摘要同时讨论随机波动率、风险中性测度、随机贴现因子与波动率风险价格。

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代码、数据与补充材料

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关注这篇论文的作者
Hyojin Han
Stanislav Khrapov
Eric Renault

重新审视杠杆效应之谜:离散时间中的识别

研究问题
在离散时间随机波动率模型中,如何区分杠杆效应与波动反馈,并判断仅凭资产收益能否识别波动率风险价格?
方法与思路
作者扩展离散时间随机波动率模型,在指数仿射随机贴现因子下保持测度变换的分布结构,并推导闭式联系函数。
材料陈述的贡献
摘要称在该模型中,仅凭收益识别波动率风险价格当且仅当存在杠杆效应;效应接近零时,联系函数趋平使估计困难。
阅读关注点
应核对历史与风险中性参数的映射、识别结论的模型前提,以及联系函数近乎平坦时是否提供识别稳健推断方法。
阅读边界
仅据所给出版社摘要,该识别结论只能按所述模型理解;摘要未说明稳健推断的具体实现,也不能据此确认普遍可识别性。

原文摘要

The term “leverage effect,” as coined by Black (1976), refers to the tendency of an asset’s volatility to be negatively correlated with the asset’s return. Aït-Sahalia et al. (2013) refer to the “leverage effect puzzle” as the fact that, in spite of a broad agreement that the effect should be present, it is hard to identify empirically. For this purpose, we propose an extension with leverage effect of the discrete time stochastic volatility model of Darolles et al. (2006). This extension is shown to be the natural discrete time analog of the Heston (1993) option pricing model. It shares with Heston (1993) the advantage of structure preserving change of measure: with an exponentially affine stochastic discount factor, the historical and the risk neutral models belong to the same family of joint probability distributions for return and volatility processes. The discrete time approach allows us to make the role of various parameters more transparent: leverage versus volatility feedback effect, connection with daily realized volatility, impact of leverage on the volatility smile, etc. Even more importantly it sheds some new light on the identification of leverage effect and of the various risk premium parameters through link functions in closed form. The price of volatility risk is identified from underlying asset return data, even without option price data, if and only if leverage effect is present. However, the link functions are almost flat if the leverage effect is close to zero, making estimation of the volatility risk price difficult and paving the way for identification robust inference.

笔记与分组
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