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Arbitrage-free approximation of call price surfaces and input data risk

机译:呼叫价格面和输入数据风险的无套利近似

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摘要

In this paper we construct arbitrage-free call price surfaces from observed market data by locally constrained least squares approximations. The algorithm computes derivatives of the call surface accurately so that implied volatility, local volatility and transition probability density can be obtained at no additional cost. Observed input data are afflicted by a price uncertainty due to the bid-ask spread, quote imprecision and non-synchrony and cause an input data risk on the computed call surface and subsequently on the implied volatility surface. We model the input risk and perform an analysis to study and measure the effect of the input risk on the surfaces. With this analysis we can determine the trustworthiness of the computed results and their implications for option pricing a posteriori.
机译:在本文中,我们通过局部约束的最小二乘近似,从观察到的市场数据中构建了无套利的看涨价格面。该算法可精确计算呼叫面的导数,因此无需额外成本即可获得隐含波动率,局部波动率和转移概率密度。观察到的输入数据由于买卖差价,报价不精确和不同步而受到价格不确定性的困扰,并在计算出的看涨期权面和随后的隐含波动率面上造成输入数据风险。我们对输入风险建模并进行分析以研究和衡量输入风险在表面上的影响。通过这种分析,我们可以确定计算结果的可信度及其对后验期权定价的影响。

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