多标的资产期权定价的Monte Carl模拟方法及其应用研究

鉴于一般的偏微分解析方法和传统数值方法处理高维期权定价问题存在很大困难,本文在单标的资产价格随机模型的基础上,推导了具相关性的多标的资产价格的随机过程公式,以此构造蒙特卡罗模拟高维欧式期权定价的随机模型,给出模拟算法,并分析了影响蒙特卡罗模拟效果的几个关键因素,模拟算例的结果显示模拟效果较好.     

篮子期权定价的蒙特卡罗方法研究

篮子期权是多标的资产的一个投资组合期权,随着投资者对其投资组合分散化日益增长的要求,人们对这种投资组合期权的需求也不断增加.当维数不断增大时,运用蒙特卡罗模拟法来定价相对来说更可行.但是随着维数的增多,模拟的效率将大幅度下降,因此对模拟进行适当的改进是十分必要的.本文在给出篮子期权定价的蒙特卡罗模拟模型基础上,应用方差减少技术中的控制变量法进行改进,并以欧式看涨篮子期权为例,进行了模拟分析.     

最优多因子仿射利率期限结构模型选择

在利用NS模型估计出市场即期利率的基础上,采用卡尔曼滤波方法对多因子Vasieck和CIR模型进行参数估计,最后运用蒙特卡罗模拟方法对交易所国债价格进行模拟,并与实际价格进行比较,进而确定了符合我们国债市场的最优多因子仿射利率期限结构模型。研究结果表明：多因子CIR模型对数据的拟合效果及对国债价格模拟效果要明显优于多因子Vasicek模型;对于多因子CIR模型而言,因子个数增加并没有提高模型的价格模拟效果;两因子CIR模型具有最优的国债价格模拟效果。     

多资产大宗商品期权定价研究

基于大宗商品收益率与便利收益服从均值回复过程的假设,建立带协整效应的多资产大宗商品期权定价模型,求解多资产大宗商品期权价格的解析解,将大宗商品期权定价推广到更一般情况.结果表明:标的资产收益率增加,期权价格上升,替代品期权价格下降;标的资产的便利收益增加,期权价格下降,相应替代品的期权价格上升.     

用SDP研究欧式看涨期权的定价问题

一、引言 给定标的资产的信息,研究这个标的资产的衍生证券的价格是经济研究的一个核心问题,假定标的资产遵循布朗运动,运用无套利理论,Black-Scholes公式给了我们一个非常明确的答案,这时候也会产生一个问题,对于标的资产价格变动不做任何假设的话,仅仅使用无套利理论,那么:已知标的资产的K个时刻的价格,由此得出衍生证券一般收益函数,问这个衍生证券价格的最可能的最好的界是多少?     

权证上市对标的股票的影响——基于中国股市的分析与实证

本文基于连续时间动态资产定价模型,推导出权证作为一种新的、依赖于标的股票的风险资产上市交易后,标的股票均衡价格变动的一般性结果,揭示出标的股票资产定价机制必然发生根本性改变;通过建立标的股票收益率与波动率的计量模型验证理论结果;实证考察,在中国,权证上市交易后对标的股票收益率及其波动性的影响,最后得出关于标的股票收益率各方面特征变化的完整结论,作为监管部门与投资者决策的参考依据。     

基于Copula GARCH的多资产期权定价

自从Black和Scholes在1976年开创性的提出了BS期权定价公式以来,期权定价理论得到了极大的发展,而在其后的研究中发展基于BS公式的隐含波动率对于执行价格具有类似微笑的曲线,其原因是标的资产的过程并不是BS公式所假设的几何布朗运动。Engle提出的ARCH模型和Bollerslev提出的GARCH模型能对标的资产的收益率序列进行很好的描述,因此将GARCH模型引入期权定价。在多资产期权定价研究当中,最为关键的是标的资产之间的依赖关系,关于依赖关系的最有力的就是Copula理论,而Copula的一大优势就是可以将边缘分布和联合分布分开,可以分别考虑边缘分布和数据的相关结构。本文设想将多元GARCH引入多资产期权定价中,但是一般的多元GARCH的系数过多而不易估计,另一方面模型的灵活程度较小,所以用一元的GARCH模型分别对各个标的资产的收益率序列进行建模,再用Copula将各个资产的分布联接起来,这便是Copula based MGARCH模型。接下来便可以通过Monte Carlo模拟对期权进行定价。     

基于Copula GARCH的多资产期权定价

自从Black和Scholes在1976年开创性的提出了BS期权定价公式以来,期权定价理论得到了极大的发展,而在其后的研究中发展基于BS公式的隐含波动率对于执行价格具有类似微笑的曲线,其原因是标的资产的过程并不是BS公式所假设的几何布朗运动。Engle提出的ARCH模型和Bollerslev提出的GARCH模型能对标的资产的收益率序列进行很好的描述,因此将GARCH模型引入期权定价。在多资产期权定价研究当中,最为关键的是标的资产之间的依赖关系,关于依赖关系的最有力的就是Copula理论,而Copula的一大优势就是可以将边缘分布和联合分布分开,可以分别考虑边缘分布和数据的相关结构。本文设想将多元GARCH引入多资产期权定价中,但是一般的多元GARCH的系数过多而不易估计,另一方面模型的灵活程度较小,所以用一元的GARCH模型分别对各个标的资产的收益率序列进行建模,再用Copula将各个资产的分布联接起来,这便是Copula based MGARCH模型。接下来便可以通过Monte Carlo模拟对期权进行定价。     

在股权分置改革中引入权证的意义和作用

权证是在西方近年来兴起的金融衍生工具之一，是一种允许持有人有权利但无义务以约定价格和约定时间购买或者出售约定数量标的资产的证券．持有人有权购买（出售）标的资产的权证为认购（售）权证。权证的构成要素主要有标的资产、执行价格、发行价格、兑换比例、到期日、执行方式及特别条款所组成。     

Delta套期保值在期权风险管理中的应用

国内首支股票期权上证50ETF期权经过一年多的模拟测试后,于2015年2月9日在上海证券交易所上市,宣告了中国期权时代的到来。期权,作为高杠杆金融衍生品,既可投机也可进行套期保值。期权的希腊字母通常用来表示期权价格对于影响期权价格的因素的敏感程度,是期权交易中重要的风险管理指标。本文将运用希腊字母Delta系数中性对冲的套期保值策略对中闲股市进行模拟研究,分别考虑以下三种情形（1）每天对标的资产进行对冲,（2）标的资产的价格变动超出上一次调整时价格的1％再进行对冲,（3）标的资产价格比上一次调整时下降了0．3％或上升了1.5％时再进行对冲。我们分别计算这三种对冲方式的成本,分析其差别,选出相对较优的对冲方式,并给出合理的解释。     

美式期权的Monte Carlo模拟法定价理论及应用

近年来随着计算机技术的飞速发展,美式期权的Monte Carlo模拟法定价取得了实质性的突破。本文分析介绍了美式期权的Monte Carlo模拟法定价理论及在此基础上推导出的线性回归MonteCarlo模拟法定价公式及其在实际的应用。     

On an efficient multiple time step Monte Carlo simulation of the SABR model

In this paper, we will present a multiple time step Monte Carlo simulation technique for pricing options under the Stochastic Alpha Beta Rho model. The proposed method is an extension of the one time step Monte Carlo method that we proposed in an accompanying paper Leitao et al. [Appl. Math. Comput. 2017, 293, 461–479], for pricing European options in the context of the model calibration. A highly efficient method results, with many very interesting and nontrivial components, like Fourier inversion for the sum of log-normals, stochastic collocation, Gumbel copula, correlation approximation, that are not yet seen in combination within a Monte Carlo simulation. The present multiple time step Monte Carlo method is especially useful for long-term options and for exotic options.     

Calibration and advanced simulation schemes for the Wishart stochastic volatility model

In this article, we deal with calibration and Monte Carlo simulation of the Wishart stochastic volatility model. Despite the analytical tractability of the considered model, being of affine type, the implementation of Wishart-based stochastic volatility models poses non-trivial challenges from a numerical point of view. The goal of this article is to overcome these problems providing efficient numerical schemes for Monte Carlo simulations. Moreover, a fast and accurate calibration procedure is proposed.     

Regression Modeling for the Valuation of Large Variable Annuity Portfolios

Variable annuities are insurance products that contain complex guarantees. To manage the financial risks associated with these guarantees, insurance companies rely heavily on Monte Carlo simulation. However, using Monte Carlo simulation to calculate the fair market values of these guarantees for a large portfolio of variable annuities is extremely time consuming. In this article, we propose the class of GB2 distributions to model the fair market values of guarantees to capture the positive skewness typically observed empirically. Numerical results are used to demonstrate and evaluate the performance of the proposed model in terms of accuracy and speed.     

A Stochastic Correlation Model with Mean Reversion for Pricing Multi-Asset Options

We set up a new kind of model to price the multi-asset options. A square root process fluctuating around its mean value is introduced to describe the random evolution of correlation between two assets. In this stochastic correlation model with mean reversion term, the correlation is a random walk within the region from −1 to 1, and it is centered around its equilibrium value. The trading strategy to hedge the correlation risk is discussed. Since a solution of high-dimensional partial differential equation may be impossible, the Quasi-Monte Carlo and Monte Carlo methods are introduced to compute the multi-asset option price as well. Taking a better-of two asset rainbow as an example, we compare our results with the price obtained by the Black–Scholes model with constant correlation.     

Unconditional return disturbances: A non-parametric simulation approach

Simulation methods are extensively used in Asset Pricing and Risk Management. The most popular of these simulation approaches, the Monte Carlo, requires model selection and parameter estimation. In addition, these approaches can be extremely computer intensive. Historical simulation has been proposed as a non-parametric alternative to Monte Carlo. This approach is limited to the historical data available.In this paper, we propose an alternative historical simulation approach. Given a historical set of data, we define a set of standardized disturbances and we generate alternative price paths by perturbing the first two moments of the original path or by reshuffling the disturbances. This approach is either totally non-parametric when constant volatility is assumed; or semi-parametric in presence of GARCH(1, 1) volatility. Without a loss in accuracy, it is shown to be much more powerful in terms of computer efficiency than the Monte Carlo approach. It is also extremely simple to implement and can be an effective tool for the valuation of financial assets.We apply this approach to simulate pay off values of options on the S&P 500 stock index for the period 1982–2003. To verify that this technique works, the common back-testing approach was used. The estimated values are insignificantly different from the actual S&P 500 options payoff values for the observed period.     

The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions

In this article, we propose an efficient approach for inverting computationally expensive cumulative distribution functions. A collocation method, called the Stochastic Collocation Monte Carlo sampler (SCMC sampler), within a polynomial chaos expansion framework, allows us the generation of any number of Monte Carlo samples based on only a few inversions of the original distribution plus independent samples from a standard normal variable. We will show that with this path-independent collocation approach the exact simulation of the Heston stochastic volatility model, as proposed in Broadie and Kaya [Oper. Res., 2006, 54, 217–231], can be performed efficiently and accurately. We also show how to efficiently generate samples from the squared Bessel process and perform the exact simulation of the SABR model.     

The affine Heston model with correlated Gaussian interest rates for pricing hybrid derivatives

In this article we define a multi-factor equity–interest rate hybrid model with non-zero correlation between the stock and interest rate. The equity part is modeled by the Heston model and we use a Gaussian multi-factor short-rate process. By construction, the model fits in the framework of affine diffusion processes, allowing fast calibration to plain vanilla options. We also provide an efficient Monte Carlo simulation scheme.     

THE EFFECTS OF BORROWING ON THE RATE OF GROWTH OF CAPITAL AND THE RISK OF RUIN OF A FIRM

The research reported in this paper used Monte Carlo simulation to study the long term effects of borrowing policy on the rate of growth of capital and the risk (probability) of ruin of hypothetical firms, operating in explicitly described, realistic capital budgeting environments. The capital rationing environment is described explicitly. The debt policies modeled were based on the results of interviews with senior financial executives in eight major firms. The results indicate that three intuitively appealing ranking procedures performed equally well and all out-performed a random selection decision procedure: yielding higher rates of capital growth with lower risks of ruin. In general, an aggressive borrowing policy resulted in a higher average capital growth rate for a firm but a conservative borrowing policy resulted in a lower risk of ruin. It is believed that the results provide some interesting insights which indicate that a computer simulation model could be used to aid management in the evaluation of their capital budgeting procedures and borrowing policies.     

Simulation-based pricing of convertible bonds

We propose and empirically investigate a pricing model for convertible bonds based on Monte Carlo simulation. The method uses parametric representations of the early exercise decisions and consists of two stages. Pricing convertible bonds with the proposed Monte Carlo approach allows us to better capture both the dynamics of the underlying state variables and the rich set of real-world convertible bond specifications. Furthermore, using the simulation model proposed, we present an empirical pricing study of the US market, using 32 convertible bonds and 69 months of daily market prices. Our results do not confirm the evidence of previous studies that market prices of convertible bonds are on average lower than prices generated by a theoretical model. Similarly, our study is not supportive of a strong positive relationship between moneyness and mean pricing error, as argued in the literature.