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arXiv 2401.00539q-fin.MF
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随机波动率模型下反向期权的隐含波动率研究

On the implied volatility of Inverse options under stochastic volatility models

Elisa Alòs, Eulalia Nualart, Makar Pravosud

AI总结:

本文研究随机波动率模型下反向期权平值隐含波动率的短期行为,利用Malliavin微积分推导出隐含波动率水平与偏斜的短到期渐近公式,并将其应用于SABR、分数Bergomi模型及比特币期权实证分析。

摘要:

本文研究固定行权价的欧式反向期权在平值状态下隐含波动率的短期行为。假设资产价格服从一般随机波动率过程。利用Malliavin微积分技术(例如预见性Itô公式),我们首先计算期权在到期时间趋于零时隐含波动率的水平。然后,我们推导出隐含波动率偏斜的短到期渐近公式,该公式依赖于波动率模型的粗糙度。我们还证明,我们的结果可以容易地推广到Quanto-Inverse期权。我们将一般结果应用于SABR模型和分数Bergomi模型,并提供了一些数值模拟,证实了偏斜渐近公式的准确性。最后,我们利用在Debirit上交易的比特币期权进行了实证应用,以展示我们的理论公式如何用于建模此类期权的真实市场数据。

英文摘要:

In this paper we study short-time behavior of the at-the-money implied volatility for Inverse European options with fixed strike price. The asset price is assumed to follow a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating It^o's formula we first compute the level of the implied volatility of the option when the maturity converges to zero. Then, we find a short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We also show that our results extend easily to Quanto-Inverse options. We apply our general results to the SABR and fractional Bergomi models, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew. Finally, we provide an empirical application using Bitcoin options traded on Debirit to show how our theoretical formulas can be used to model real market data of such options.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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