基于跳扩散模型欧式期权定价的条件二叉树方法

对股票价格的跳扩散模型进行了分析,在CRR二叉树期权定价模型的基础上考虑标的股票价格发生跳跃的情况,得出基于跳扩散过程的股票期权的条件二叉树定价模型,并且证明在极限情况下,该条件二叉树模型的期权定价公式趋于Merton的解析定价公式,数值试验证实该条件二叉树模型的有效性。     

二叉树期权定价方法的一种新推广

对目前普遍使用的期权定价二叉树模型进行了分析,利用随机误差校正方法推广出了一种新型的二叉树参数模型.     

期权定价新型二叉树参数模型的构造

对目前普遍使用的期权定价二叉树模型的缺陷进行了分析,利用矩法构造出新型的二叉树参数模型.新的模型避免了负的概率并且具有很高的计算精度,因而可应用于计算各种期权的价格.     

关于无套利市场的二叉树期权定价模型性质的讨论

本文讨论了二叉树期权市场的无套利条件,引入有随机因素存在的二叉树欧式期权定价模型,并推出单阶段、多阶段情况下欧式期权的计算公式,证明了多阶段市场未定权益的重要性质.     

期权定价的新型三叉树方法

讨论了普通二叉树模型定价公式的缺陷,在新型二叉树定价模型的基础上利用原点矩和中心矩的关系得出新型三叉树定价模型公式,并且证明该三叉树模型下期权价格满足的方程是B-S方程在Δt上的一阶近似.     

GARCH模型下基于偏最小二乘的欧式股指期权定价——来自香港恒生指数期权市场的证据

目前,股指期权呼之欲出,在这种形势下,本文对股指期权定价问题进行了研究。本文首先在GARCH模型的基础上导出期权定价估值公式,其次,在GARCH欧式股指期权定价模型的基础上,融入偏最小二乘技术,给出最终的欧式股指期权的偏最小二乘定价方法。最后,对香港恒指期权进行参数估计和GARCH建模,运用新的定价方法进行期权定价。研究发现,对最终期权价格影响最大的是GARCH模型的估计值;另外整个大盘的活跃程度、投资者情绪也有不可忽视的影响。这个结论为中国顺利发展指数期权市场提供了坚实有力的定价依据。     

CEV下有交易费用的回望期权的定价研究

本文在研究服从CEV过程且无交易费用的回望期权定价模型的基础上,推导出CEV下有交易费用的回望期权定价模型,并利用变量转换和二叉树方法求解,最终给出了CEV下有交易费用的回望期权的近似解。     

带交易费用和红利的欧式期权定价的数值方法

本文在 L eland的带交易费用的欧式期权定价模型基础上 ,先推导出一般费用模型的定价公式 ,然后用二叉树图法给出了带有交易费用和红利的欧式看涨期权定价的数值方法 ,并比较了多头和空头的不同价值。     

美式看跌期权定价的控制变量法及其估值效率研究

本文基于控制变量法原理,在Black-Scholes期权定价公式的基础上,采用CV-CRR方法为美式看跌期权定价.实证分析表明,运用控制变量法可以大大改进标准二叉树方法的运算速度和估值精度,提高了估值效率.     

模糊环境下美式看跌期权的定价研究

应用模糊集理论将无风险利率和波动率进行模糊化,以梯形模糊数替代精确值,将美式期权的定价模型扩展到美式期权模糊定价模型.得到了模糊风险中性概率表达式,并在此概率测度下推导出多期二叉树模糊定价模型,以及二叉树上各节点以梯形模糊数表示的模糊期权价值,以数值模拟演示了美式看跌期权的模糊定价过程.最后分析了不同风险偏好投资者在不确定环境下的套利决策行为,结果表明风险偏好大的投资者具有较高的置信水平、较小的主观模糊期权价格以及较大的无风险套利区间.     

广义泊松回归模型的推广及其在医疗保险中应用

计数数据往往存在过离散(over-dispersed)即方差大于均值特征,若利用传统的泊松回归模型拟合数据往往会导致其参数的标准误差被低估,显著性水平被高估的错误结论。负二项回归模型、广义泊松回归模型通常被用来处理过离散特征数据。本文以两类广义泊松回归模型GP-1和GP-2模型为基础,将其推广为更为一般的GP-P形式,其中P为参数。此时,P=1或P=2,GP-P模型就退化为GP-1和GP-2模型。文中最后利用此类推广的GP-P模型处理了一组医疗保险数据,并与泊松回归模型、负二项回归模型拟合结果进行了比较。结果表明,推广后的GP-P模型的拟合效果更优。     

Software reliability assessment models based on cumulative bernoulli trial processes

The binomial software reliability growth model (SRGM) contains most existing SRGMs proposed in earlier work as special cases, and can describe every software failure-occurrence pattern in continuous time. In this paper, we propose generalized binomial SRGMs in both continuous and discrete time, based on the idea of cumulative Bernoulli trials. It is shown that the proposed models give some new unusual discrete models as well as the well-known continuous SRGMs. Through numerical examples with actual software failure data, two estimation methods for model parameters with grouped data are provided, and the predictive model performance is examined quantitatively.     

Compound negative binomial shared frailty models for bivariate survival data

In this paper, we introduce a new shared frailty model called the compound negative binomial shared frailty model with three different baseline distributions namely, Weibull, generalized exponential and exponential power distribution. To estimate the parameters involved in these models we adopt Markov Chain Monte Carlo (MCMC) approach. Also we apply these three models to a real life bivariate survival data set of McGrilchrist and Aisbett (1991) related to kidney infection and suggest a better model for the data.     

Extensions of the Ho and Lee interest-rate model to the multinomial case

The paper presents a state dependent multinomial model of intertemporal changes in the term structure of interest rates. The model is a one-factor interest-rate model within the Markov family models for short-term interest rate and it extends the Ho and Lee [J. Finance XLI (5) (1986) 1001] binomial model. We derive the theoretical basis of the multinomial model, suggest a computational framework to evaluate the model's parameters and investigate the suitability of the model for the Italian market.     

Binomial option pricing with nonidentically distributed returns and its implications

The argument of Cox, Ross, and Rubinstein for pricing options is generalized in the direction of using nonidentically distributed binomial returns as a model for the stock price process. It is found that the use of nonidentically distributed binomial returns, in the limit exhaust the class of infinitely divisible distributions. The pricing of these models are considered and it is shown that the model is a generalization of the Black-Scholes model. The use, however, of nonidentically distributed returns, it is shown, can lead to contradictions. Hence, it is argued, the models used for stock price behavior requires restrictions.     

Bayesian‐type count data models with varying coefficients: estimation and testing in the presence of overdispersion

In this paper we study varying‐coefficient models for count data. A Bayesian approach is taken to model the variability of the regression parameters. Based on a Kalman filter procedure the varying coefficients are estimated as the mode of the posterior distribution. All hyperparameters, including an overdispersion parameter in the negative binomial varying‐coefficient model (NBVC), are estimated as ML‐estimators using an EM‐type algorithm. A bootstrapping test of the fixed‐coefficient hypothesis against a varying‐coefficient alternative is proposed, which is evaluated running a simulation study. The study shows that the choice of a suitable count data model is of special importance in the framework of varying‐coefficient models. The methodology is illustrated analysing the determinants of the number of individual doctor visits. Copyright © 2001 John Wiley & Sons, Ltd.     

Bayesian quantile regression model for claim count data

Quantile regression model estimates the relationship between the quantile of a response distribution and the regression parameters, and has been developed for linear models with continuous responses. In this paper, we apply Bayesian quantile regression model for the Malaysian motor insurance claim count data to study the effects of change in the estimates of regression parameters (or the rating factors) on the magnitude of the response variable (or the claim count). We also compare the results of quantile regression models from the Bayesian and frequentist approaches and the results of mean regression models from the Poisson and negative binomial. Comparison from Poisson and Bayesian quantile regression models shows that the effects of vehicle year decrease as the quantile increases, suggesting that the rating factor has lower risk for higher claim counts. On the other hand, the effects of vehicle type increase as the quantile increases, indicating that the rating factor has higher risk for higher claim counts.     

Some geometric mixed integer-valued autoregressive (INAR) models

In this paper, we introduce some mixed integer-valued autoregressive models of orders 1 and 2 with geometric marginal distributions, denoted by MGINAR(1) and MGINAR(2), using a mixture of the well-known binomial and the negative binomial thinning. The distributions of the innovation processes are derived and several properties of the model are discussed. Conditional least squares and Yule-Walker estimators are obtained, and some numerical results of the estimations are presented. A real-life data example is investigated to assess the performance of the models.     

负二项回归模型的推广及其在分类费率厘定中的应用

分类费率厘定中最常使用的模型之一是泊松回归模型,但当损失次数数据存在过离散特征时,通常会采用负二项回归模型。本文将两参数的负二项回归模型推广到了三参数情况,并用它来解决分类费率厘定中的过离散(over-dispersion)问题。本文通过对一组汽车保险损失数据的拟合表明,三参数的负二项分布回归模型可以有效改善对实际损失数据的拟合效果。