论文标题
使用贝叶斯推理方法引起的二进制选项引起的反问题的参数识别
Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach
论文作者
论文摘要
否 - Arbitrage属性为定价金融衍生品提供了一种简单的方法。但是，即使在很短的时间内，各个领域的不同市场之间存在套利机会。通过知道存在套利财产，我们可以采用财务交易策略。本文研究了扩展的黑色 - choles模型中的反期权问题（IOP）。我们从测量数据中确定模型系数，并尝试使用贝叶斯推理方法在不同金融市场中找到套利机会，该方法作为IOP解决方案提出。参数的后概率密度函数是根据测量数据计算的。 MCMC算法的有效抽样策略使我们能够通过贝叶斯推理技术解决反问题。我们的数值结果表明，贝叶斯推论方法可以同时估计未知的趋势和波动率系数。
No--arbitrage property provides a simple method for pricing financial derivatives. However, arbitrage opportunities exist among different markets in various fields, even for a very short time. By knowing that an arbitrage property exists, we can adopt a financial trading strategy. This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in different financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data.The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.
