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1.
本文讨论了潜伏期和传染期均服从威布尔分布、易感性随机变化的一类随机流行病模型,并利用M CM C算法对潜伏期、传染期的参数和易感性的超参数作了贝叶期推断.这种分析方法比以往各种方法更适用于各类疾病.  相似文献   

2.
波动率估计是金融学的核心,波动几乎渗透金融市场的每一个领域.为了快速而精确地提取波动率,文章将比例UT变换与最小偏度单行采样技术和无迹卡尔曼滤波(UKF)算法相结合,提出一种适用于非线性高斯状态空间模型的改进的无迹卡尔曼滤波(MUKF)算法,并将该算法应用到扩散的期权定价模型中.最后通过对Heston随机波动模型进行模拟研究,发现在同时使用股票价格数据和期权数据时,可以精确地提取波动率,而且MUKF算法比UKF算法的计算时间更短.文章也对Heston模型中的波动率的波动参数进行了研究,研究发现MUKF算法可以准确地捕捉这种波动率特性.  相似文献   

3.
中国股票市场风险的实证分析研究   总被引:6,自引:1,他引:5  
李萌  叶俊 《数理统计与管理》2003,22(4):12-17,23
本文从实证角度说明了上证指数和深证成份指数存在着GARCH现象 ,并建立了沪、深两市股指波动率的IGARCH(1,1) M模型与EGARCH(1,1) M模型。将估计的IGARCH(1,1) M模型与EGARCH(1,1) M模型比较得出 ,对上证指数的波动率 ,IGARCH(1,1) M模型与EGARCH(1,1) M模型的模拟效果基本相同 ,而对深证成份指数的波动率 ,IGARCH M模型要略优于EGARCH M模型。同时还对两市的股指收益的波动率进行了预测分析  相似文献   

4.
利用M arkov cha in M on te C arlo技术对可分离的下三角双线性模型进行B ayes分析.由于参数联合后验密度的复杂性,我们导出了所有的条件后验分布,以便利用G ibbs抽样器方法抽取后验密度的样本.特别地,由于从模型的方向向量的后验分布中直接抽样是困难的,我们特别设计了一个M etropolis-H astings算法以解决该难题.我们用仿真的方法验证了所建议方法的有效性,并成功应用于分析实际数据.  相似文献   

5.
一些流行的技术指标(例如布林带,RSI,ROC等)被股市交易者广为使用.交易者将每日(小时,周,……)的实际股价作为计算某个技术指标的样本,通过观察相关频率来指导投资.技术指标的有效性已在广泛的应用中得到了验证.我们已经证明在Black-Scholes模型下,某些技术指标有许多有用的统计性质.作为更一般的情况,随机波动率模型在金融数学中得到了广泛的讨论.本文基于随机波动率模型对技术指标的统计性质进行了研究.研究结果表明,如果股票价格服从随机波动率模型,则技术指标的合理性可以得到有力的证明,从这个角度我们为技术分析奠定理论基础.  相似文献   

6.
随机波动率与双指数跳扩散组合模型的美式期权定价   总被引:3,自引:0,他引:3  
在股价满足Cox-Ingersoll-Ross(CIR)随机波动率与Kou的双指数跳扩散组合模型下,利用随机分析方法讨论了美式看跌期权函数及最佳实施边界的性质.应用一阶线性近似实施边界获得了期权价格的拟解析式和实施边界满足的非线性方程.进一步,应用梯形法离散处理方程式内积分表达式,建立了期权最佳实施边界和价格的数值算法.最后分别给出了常数波动率或CIR随机波动率的数值实例.  相似文献   

7.
本文研究Dirac方程-iΣαkku+aβu+M(x)u=g(x,|u|)u的解,其中M(x)是位势函数,g(x,|u|)u在无穷远处关于u是超线性的.本文用变分法来研究这一问题.借助于与此方程的"极限方程"相关的某个辅助系统,构造了变分泛函ΦM的环绕水平,使得建立在ΦM环绕结构上的极小极大值CM满足0〈CM〈C,这里C是"极限方程"的最小能量.从而可以证明(C)c条件对所有c〈C成立,因此得到了方程的最小能量解.  相似文献   

8.
有限群的Deskins极大完备   总被引:1,自引:0,他引:1  
李世荣 《数学年刊A辑》2003,24(2):145-150
对于有限群G的一个极大子群M,Deskins称子群C为M的一个完备,如果C M,但C的G-不变真子群包含在M中.令I(M)表示M的所有完备之集.I(M)的一个极大元叫做M的一个极大完备.利用极大完备,本文获得关于群的可解性和超可解性某些新的刻划.  相似文献   

9.
对于有限群G的一个极大予群M,Deskins称子群C为M的一个完备,如果C(?)M,但C的G-不变真子群包含在M中.令I(M)表示M的所有完备之集. I(M)的一个极大元叫做M的一个极大完备.利用极大完备,本文获得关于群的可解性和超可解性某些新的刻划.  相似文献   

10.
廉庆荣  金志英 《计算数学》1987,9(2):200-205
1971年,M.H.C.Paardekooper将对称阵的Jacobi思想推广到反对称阵,给出一个求反对称阵特征值的实用算法(简称P算法).但P算法仅考虑到矩阵的反对称性,未利用其纯虚数特征值共轭成对的性质,而且也未探讨特征值共轭对相重与否对运算量的影响.鉴于此,本文提出一个新算法,其运算量比P算法少得多. 我们先用Givens相似变换(其快速算法见§3之3.2)化反对称阵A为三对角反对称  相似文献   

11.
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit nonidentifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate dataset illustrates the superior performance of the new approach for real-world data. Supplementary materials for this article are available online.  相似文献   

12.
针对具有Markov区制转移的、波动均值状态相依的随机波动模型,基于贝叶斯分析,我们推导并给出了对区制转移随机波动模型的MCMC估计方法,其中对参数估计采用Gibbs抽样方法,对潜在对数波动和区制的状态变量估计采用"向前滤波、向后抽样"的多步移动方法;利用该模型,对我国上证综指周收益率进行了实证分析,发现对沪市波动性有较好的描述,捕捉了波动的时变性、聚类性和非线性特征,同时刻画了沪市的高低波动状态转换过程。  相似文献   

13.
Gaussian graphical models (GGMs) are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of GGMs extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous subgroups. In this article, we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable GGMs. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo (MCMC) algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the MCMC algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which MCMC algorithms are too slow to be practically useful.  相似文献   

14.
The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.  相似文献   

15.
This paper discusses practical Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma laws. Estimation is based on a parameterization which is derived from the Rosiński representation, and has the advantage of being a non-centered parameterization. The parameterization is based on a marked point process, living on the positive real line, with uniformly distributed marks. We define a Markov chain Monte Carlo (MCMC) scheme which enables multiple updates of the latent point process, and generalizes single updating algorithm used earlier. At each MCMC draw more than one point is added or deleted from the latent point process. This is particularly useful for high intensity processes. Furthermore, the article deals with superposition models, where it discuss how the identifiability problem inherent in the superposition model may be avoided by the use of a Markov prior. Finally, applications to simulated data as well as exchange rate data are discussed.  相似文献   

16.
In the following article, we investigate a particle filter for approximating Feynman–Kac models with indicator potentials and we use this algorithm within Markov chain Monte Carlo (MCMC) to learn static parameters of the model. Examples of such models include approximate Bayesian computation (ABC) posteriors associated with hidden Markov models (HMMs) or rare-event problems. Such models require the use of advanced particle filter or MCMC algorithms to perform estimation. One of the drawbacks of existing particle filters is that they may “collapse,” in that the algorithm may terminate early, due to the indicator potentials. In this article, using a newly developed special case of the locally adaptive particle filter, we use an algorithm that can deal with this latter problem, while introducing a random cost per-time step. In particular, we show how this algorithm can be used within MCMC, using particle MCMC. It is established that, when not taking into account computational time, when the new MCMC algorithm is applied to a simplified model it has a lower asymptotic variance in comparison to a standard particle MCMC algorithm. Numerical examples are presented for ABC approximations of HMMs.  相似文献   

17.
Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.  相似文献   

18.
In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.  相似文献   

19.
The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte-Carlo methods. We study here an advanced version of familiar Markov-chain Monte-Carlo (MCMC) algorithms that sample from target distributions defined as change of measures from Gaussian laws on general Hilbert spaces. Such a model structure arises in several contexts: we focus here at the important class of statistical models driven by diffusion paths whence the Wiener process constitutes the reference Gaussian law. Particular emphasis is given on advanced Hybrid Monte-Carlo (HMC) which makes large, derivative-driven steps in the state space (in contrast with local-move Random-walk-type algorithms) with analytical and experimental results. We illustrate its computational advantages in various diffusion processes and observation regimes; examples include stochastic volatility and latent survival models. In contrast with their standard MCMC counterparts, the advanced versions have mesh-free mixing times, as these will not deteriorate upon refinement of the approximation of the inherently infinite-dimensional diffusion paths by finite-dimensional ones used in practice when applying the algorithms on a computer.  相似文献   

20.
A threshold stochastic volatility (SV) model is used for capturing time-varying volatilities and nonlinearity. Two adaptive Markov chain Monte Carlo (MCMC) methods of model selection are designed for the selection of threshold variables for this family of SV models. The first method is the direct estimation which approximates the model posterior probabilities of competing models. Using parallel MCMC sampling to estimate these probabilities, the best threshold variable is selected with the highest posterior model probability. The second method is to use the deviance information criterion to compare among these competing models and select the best one. Simulation results lead us to conclude that for large samples the posterior model probability approximation method can give an accurate approximation of the posterior probability in Bayesian model selection. The method delivers a powerful and sharp model selection tool. An empirical study of five Asian stock markets provides strong support for the threshold variable which is formulated as a weighted average of important variables.  相似文献   

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