Option Pricing in Illiquid Markets with Jumps

Q3 Mathematics
José M. T. S. Cruz, D. Ševčovič
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引用次数: 4

Abstract

ABSTRACT The classical linear Black–Scholes model for pricing derivative securities is a popular model in the financial industry. It relies on several restrictive assumptions such as completeness, and frictionless of the market as well as the assumption on the underlying asset price dynamics following a geometric Brownian motion. The main purpose of this paper is to generalize the classical Black–Scholes model for pricing derivative securities by taking into account feedback effects due to an influence of a large trader on the underlying asset price dynamics exhibiting random jumps. The assumption that an investor can trade large amounts of assets without affecting the underlying asset price itself is usually not satisfied, especially in illiquid markets. We generalize the Frey–Stremme nonlinear option pricing model for the case the underlying asset follows a Lévy stochastic process with jumps. We derive and analyze a fully nonlinear parabolic partial-integro differential equation for the price of the option contract. We propose a semi-implicit numerical discretization scheme and perform various numerical experiments showing the influence of a large trader and intensity of jumps on the option price.
非流动性跳跃市场中的期权定价
经典的布莱克-斯科尔斯线性衍生证券定价模型是金融行业中比较流行的模型。它依赖于几个限制性假设,如市场的完整性和无摩擦性,以及对基础资产价格动态遵循几何布朗运动的假设。本文的主要目的是推广经典的Black-Scholes衍生品证券定价模型,该模型考虑了由于大型交易商对标的资产价格动态表现出随机跳跃的影响而产生的反馈效应。投资者可以交易大量资产而不影响标的资产价格本身的假设通常是不满足的,特别是在非流动性市场。对于标的资产服从有跳跃的随机过程的情况,我们推广了frey - streme非线性期权定价模型。我们推导并分析了一个关于期权合约价格的完全非线性抛物型偏积分微分方程。我们提出了一种半隐式数值离散化方案,并进行了各种数值实验,证明了大型交易者和跳跃强度对期权价格的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Mathematical Finance
Applied Mathematical Finance Economics, Econometrics and Finance-Finance
CiteScore
2.30
自引率
0.00%
发文量
6
期刊介绍: The journal encourages the confident use of applied mathematics and mathematical modelling in finance. The journal publishes papers on the following: •modelling of financial and economic primitives (interest rates, asset prices etc); •modelling market behaviour; •modelling market imperfections; •pricing of financial derivative securities; •hedging strategies; •numerical methods; •financial engineering.
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