论文标题
非平衡偏度,市场危机和期权定价:具有超对称性的非线性Langevin市场模型
Non-Equilibrium Skewness, Market Crises, and Option Pricing: Non-Linear Langevin Model of Markets with Supersymmetry
论文作者
论文摘要
本文介绍了使用Langevin方法的非线性动力学动力学模型。由于相互作用潜力的非线性性,该模型允许大小回报波动的机制。 Langevin动力学映射到等效量子机械(QM)系统。从超对称量子力学(SUSY QM)借用的想法,该QM系统的参数基态波函数(WF)被用作模型的直接输入,这也固定了非线性langevin的潜力。使用两个组分的高斯混合物作为具有不对称双井电位的基态WF,可产生具有可解释参数的可拖动的低参数模型,称为NES(非平衡偏度)模型。然后使用超对称性(SUSY)以分析性易处理的方式来找到模型的时间依赖性解。其他近似值产生了NES模型的最终实用版本,其中三个组件高斯混合物给出了房地产和风险中立的回报分布。这可以通过三个黑色choles价格的混合物在NES模型中产生封闭形式的近似,从而为良性或遇险市场环境提供准确的校准,同时仅使用单个波动率参数。这些结果与大多数其他选项定价模型(例如本地,随机或粗糙的波动率模型)形成鲜明对比,这些模型需要更复杂的噪声规格以适合市场数据。
This paper presents a tractable model of non-linear dynamics of market returns using a Langevin approach. Due to non-linearity of an interaction potential, the model admits regimes of both small and large return fluctuations. Langevin dynamics are mapped onto an equivalent quantum mechanical (QM) system. Borrowing ideas from supersymmetric quantum mechanics (SUSY QM), a parameterized ground state wave function (WF) of this QM system is used as a direct input to the model, which also fixes a non-linear Langevin potential. Using a two-component Gaussian mixture as a ground state WF with an asymmetric double well potential produces a tractable low-parametric model with interpretable parameters, referred to as the NES (Non-Equilibrium Skew) model. Supersymmetry (SUSY) is then used to find time-dependent solutions of the model in an analytically tractable way. Additional approximations give rise to a final practical version of the NES model, where real-measure and risk-neutral return distributions are given by three component Gaussian mixtures. This produces a closed-form approximation for option pricing in the NES model by a mixture of three Black-Scholes prices, providing accurate calibration to option prices for either benign or distressed market environments, while using only a single volatility parameter. These results stand in stark contrast to the most of other option pricing models such as local, stochastic, or rough volatility models that need more complex specifications of noise to fit the market data.