An Option Pricing Model with Probability Measure Ambiguity

Yu Liu, Hao Wang, Lihong Zhang
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Abstract

This paper develops an option pricing model that admits probability measure ambiguity. It formulates a piecewise risk-ambiguity-neutral probability density function and derives analytical pricing formula. Options and their underlying assets are exposed to different scopes of ambiguity that cannot be hedged, implying that options are generically non-redundant assets and have different Sharpe ratios than the underlying assets. Introduction of probability measure ambiguity reduces the in-sample and 1-day (5-day) out-of-sample pricing errors of the Black-Scholes-Merton model by 80% and 66% (61%) in pricing S&P 500 index options, and remarkably alleviates volatility smile. Option-implied market ambiguity premium is counter-cyclical and contains distinct information compared to VIX.
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具有概率测度模糊性的期权定价模型
本文建立了一个允许概率测度模糊的期权定价模型。建立了分段风险模糊中性的概率密度函数,推导出分析定价公式。期权及其标的资产面临不同范围的不确定性,无法对冲,这意味着期权通常是非冗余资产,其夏普比率与标的资产不同。概率测度模糊性的引入使Black-Scholes-Merton模型在标普500指数期权定价中的样本内和1天(5天)样本外定价误差分别降低了80%和66%(61%),显著缓解了波动微笑。期权隐含市场模糊性溢价是逆周期的,与波动率指数相比包含不同的信息。
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