拟蒙特卡罗法在亚洲期权定价中的应用

亚洲期权是场外交易中几种最受欢迎的新型期权之一,但它的价格却没有解析表达式,到目前为止,亚洲期权的定价仍是个公开问题.本文采用拟蒙特卡罗法中的Halton序列来估计它的价格,数值结果表明当观察点的个数N13时,它比蒙特卡罗法要好.本文还利用MATLAB程序生成了随机Halton序列,并将它与控制变量法结合起来估计亚洲期权的价格,估计值标准差的比较表明它在大多情况下比相应的蒙特卡罗法的估计效果要好.     

Efficient Pricing of Derivatives on Assets with Discrete Dividends

It is argued that due to inconsistencies in existing methods to approximate the prices of equity options on assets which pay out fixed cash dividends at future dates, a new approach to this problem may be useful. Logically consistent methods which are guaranteed to exclude arbitrage exist, but they are not very popular in practice due to their computational complexity. An algorithm is defined which is easy to understand, computationally efficient, and which guarantees to generate prices which exclude arbitrage possibilitites. It is shown that for the method to work a mild uniform convergence condition must be satisfied and this condition is indeed satisfied for standard European and American options. Numerical results testify to the accuracy and flexibility of the method.     

Valuation of power plants by utility indifference and numerical computation

This paper presents a real option valuation model of a power plant, which accounts for physical constraints and market incompleteness. Switching costs, minimum on-off times, ramp rates, or non-constant heat rates are important characteristics that can lead, if neglected, to overestimated values. The existence of non-hedgeable uncertainties is also a feature of energy markets that can impact assets value. We use the utility indifference approach to define the value of the physical asset. We derive the associated mixed optimal switching-control problem and provide a characterization of its solution by means of a coupled system of reflected Backward Stochastic Differential Equations (BSDE). We relate this system to a system of variational inequalities, and we provide a numerical comparative study by implementing BSDE simulation algorithms, and PDE finite differences schemes.     

Optimal Partial Proxy Method for Computing Gammas of Financial Products with Discontinuous and Angular Payoffs

We extend the limit optimal partial proxy method to compute second-order sensitivities of financial products with discontinuous or angular payoffs by Monte Carlo simulation. The methodology is optimal in terms of minimizing the variance of the likelihood ratio weight. Applications are presented for both equity and interest-rate products with discontinuous payoff structures. The first-order optimal partial proxy method is also implemented to calculate the Hessians of insurance products with angular payoffs. Numerical results are presented which demonstrate the speed and efficacy of the method.     

基于GARCH-GH模型的上证50ETF期权定价研究

上证50ETF期权是中国推出的首支股票期权.为描述上证50ETF收益率偏态、尖峰、时变波动率等特征,结合GARCH模型和广义双曲(Generalized Hyperbolic,GH)分布两方面的优势,建立GARCH-GH模型为上证50ETF期权定价.在等价鞅测度下,利用蒙特卡罗方法估计上证50ETF欧式认购期权价格.实证表明,相比较Black-Scholes模型和GARCH-Gaussian模型,GARCH-GH模型得到的结果更接近于上证50ETF期权的实际价格,其定价误差最小.     

基于混合规避策略的期权定价及其数值分析

考虑现实市场中红利的存在、波动率等参数随时间变化以及交易时间不连续产生的对冲风险不可忽略,研究离散时间、支付红利条件下基于混合规避策略的期权定价模型.由平均自融资-极小方差规避策略得到相应欧式看涨期权定价方程,并且分别使用偏微分方法和概率论方法得到统一的闭形解.数值分析表明,与经典的期权定价模型相比,新模型中的期权价格更接近对冲成本.     

基于随机双曲折现的投资消费及效用无差别定价

在随机双曲折现条件下,显式地给出了具有指数函数(CARA)效用的最优跨期消费与投资组合;在非完备市场下,显式给出了基于CARA效用的收益流的效用无差别价格.结果表明:最优投资比例以及收益流的价值不受随机双曲折现因子的影响;在低折扣阶段,本文的最优消费水平高于Merton模型下的对应值,低折扣时期越短或高低折扣值相差越大,消费差距越明显.     

Research on Flood Loss Distribution and Catastrophe Bond Pricing Based on Bayesian Inference

Taking flood catastrophe risk in China as the research background, aiming at the characteristics of flood loss ``low frequency and high loss', Bayesian inference method is used to fit the loss distribution, and Bayesian inference is used to obtain the loss frequency distribution and loss quota distribution of flood in China. On this basis, Monte Carlo simulation method is used to calculate the probability distribution of annual flood loss in China under different trigger conditions, and then CAPM is used to study the pricing of flood catastrophe bonds in China. It is concluded that under different trigger conditions, as the trigger value increases gradually, the corresponding trigger is triggered. Comparing the three types of bonds, it can be found that the price of bonds decreases with the decrease of principal guarantee ratio and the increase of principal loss ratio, that is, the investment risk is directly proportional to the return, which provides reference for issuing flood catastrophe bonds in China.     

On Modeling Change Points in Non-Homogeneous Poisson Processes

Failures in repairable systems are often described by means of non-homogeneous Poisson processes, identified by their intensity and mean value functions. Intervention on the systems are likely to modify their reliability, and changes in intensities and mean value functions are therefore induced. We consider different scenarios in which interventions take places and propose models describing each of them. Bayesian analyses, relying on Markov-chain Monte Carlo methods, are illustrated along with applications to simulated and real, widely-known, data.     

Cluster Algorithms for Spatial Point Processes with Symmetric and Shift Invariant Interactions

Abstract

In this article, Swendsen–Wang–Wolff algorithms are extended to simulate spatial point processes with symmetric and stationary interactions. Convergence of these algorithms is considered. Some further generalizations of the algorithms are discussed. The ideas presented in this article can also be useful in handling some large and complicated systems.     

A micro-level claim count model with overdispersion and reporting delays

The accurate estimation of outstanding liabilities of an insurance company is an essential task. This is to meet regulatory requirements, but also to achieve efficient internal capital management. Over the recent years, there has been increasing interest in the utilisation of insurance data at a more granular level, and to model claims using stochastic processes. So far, this so-called ‘micro-level reserving’ approach has mainly focused on the Poisson process.In this paper, we propose and apply a Cox process approach to model the arrival process and reporting pattern of insurance claims. This allows for over-dispersion and serial dependency in claim counts, which are typical features in real data. We explicitly consider risk exposure and reporting delays, and show how to use our model to predict the numbers of Incurred-But-Not-Reported (IBNR) claims. The model is calibrated and illustrated using real data from the AUSI data set.     

On reparametrization and the Gibbs sampler

Gibbs samplers derived under different parametrizations of the target density can have radically different rates of convergence. In this article, we specify conditions under which reparametrization leaves the convergence rate of a Gibbs chain unchanged. An example illustrates how these results can be exploited in convergence rate analyses.     

Average cost Markov control processes: stability with respect to the Kantorovich metric

We study perturbations of a discrete-time Markov control process on a general state space. The amount of perturbation is measured by means of the Kantorovich distance. We assume that an average (per unit of time on the infinite horizon) optimal control policy can be found for the perturbed (supposedly known) process, and that it is used to control the original (unperturbed) process. The one-stage cost is not assumed to be bounded. Under Lyapunov-like conditions we find upper bounds for the average cost excess when such an approximation is used in place of the optimal (unknown) control policy. As an application of the found inequalities we consider the approximation by relevant empirical distributions. We illustrate our results by estimating the stability of a simple autoregressive control process. Also examples of unstable processes are provided.     

Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing

A new, simple algorithm of order 2 is presented to approximate weakly stochastic differential equations. It is then applied to the problem of pricing Asian options under the Heston stochastic volatility model.

2000 Mathematics Subject Classification, 65C30, 65C05.     

Posterior Simulation with Priors Specified on Functionals

Abstract

Many Bayesian analyses use Markov chain Monte Carlo (MCMC) techniques. MCMC techniques work fastest (per iteration) when the prior distribution of the parameters is chosen conveniently, such as a conjugate prior. However, this is sometimes at odds with the prior desired by the investigator. We describe two motivating examples where nonconjugate priors are preferred. One is a Dirichlet process where it is difficult to implement alternative, nonconjugate priors. We develop a method that allows computation to be done with a convenient prior but adjusts the equilibrium distribution of the Markov chain to be the posterior distribution from the desired prior. In addition to allowing more freedom in choosing prior distributions, the method enables the investigator to perform quick sensitivity analyses, even in nonparametric settings.     

An Efficient Monte Carlo Method for Optimal Control Problems with Uncertainty

A general framework is proposed for what we call the sensitivity derivative Monte Carlo (SDMC) solution of optimal control problems with a stochastic parameter. This method employs the residual in the first-order Taylor series expansion of the cost functional in terms of the stochastic parameter rather than the cost functional itself. A rigorous estimate is derived for the variance of the residual, and it is verified by numerical experiments involving the generalized steady-state Burgers equation with a stochastic coefficient of viscosity. Specifically, the numerical results show that for a given number of samples, the present method yields an order of magnitude higher accuracy than a conventional Monte Carlo method. In other words, the proposed variance reduction method based on sensitivity derivatives is shown to accelerate convergence of the Monte Carlo method. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the proposed method is a fraction of the total time of the Monte Carlo method.     

Discounted cost optimality problem: stability with respect to weak metrics

We find inequalities to estimate the stability (robustness) of a discounted cost optimization problem for discrete-time Markov control processes on a Borel state space. The one stage cost is allowed to be unbounded. Unlike the known results in this area we consider a perturbation of transition probabilities measured by the Kantorovich metric, closely related to the weak convergence. The results obtained make possible to estimate the vanishing rate of the stability index when approximation is made through empirical measures.     

Markov Decision Processes with Variance Minimization: A New Condition and Approach

Abstract

This article deals with the limiting average variance criterion for discrete-time Markov decision processes in Borel spaces. The costs may have neither upper nor lower bounds. We propose another set of conditions under which we prove the existence of a variance minimal policy in the class of average expected cost optimal stationary policies. Our conditions are weaker than those in the previous literature. Moreover, some sufficient conditions for the existence of a variance minimal policy are imposed on the primitive data of the model. In particular, the stochastic monotonicity condition in this paper has been first used to study the limiting average variance criterion. Also, the optimality inequality approach provided here is different from the “optimality equation approach” widely used in the previous literature. Finally, we use a controlled queueing system to illustrate our results.     

Bayesian modeling of retrospective time-to-pregnancy data with digit preference bias

The study of factors affecting human fertility is an important problem affording interesting statistical and computational challenges. Analyses of human fertility rates must cope with extra variability in fecundability parameters as well as a host of covariates ranging from the obvious, such as coital frequency, to the subtle, like the smoking habits of the female’s mother. In retrospective human fecundity studies, researchers ask couples the time required to conceive. This time-to-pregnancy data often exhibits digit preference bias, among other problems. We introduce computationally intensive models with sufficient flexibility to represent such bias and other causes yielding a similar lack of monotonicity in conception probabilities.