Monte Carlo Kalman filter and smoothing for multivariate discrete state space models

The author studies state space models for multivariate binomial time series, focussing on the development of the Kalman filter and smoothing for state variables. He proposes a Monte Carlo approach employing the latent variable representation which transplants the classical Kalman filter and smoothing developed for Gaussian state space models to discrete models and leads to a conceptually simple and computationally convenient approach. The method is illustrated through simulations and concrete examples.     

A family of models for uniform and serial dependence in repeated measurements studies

Data arising from a randomized double-masked clinical trial for multiple sclerosis have provided particularly variable longitudinal repeated measurements responses. Specific models for such data, other than those based on the multivariate normal distribution, would be a valuable addition to the applied statistician's toolbox. A useful family of multivariate distributions can be generated by substituting the integrated intensity of one distribution into a second (outer) distribution. The parameters in the second distribution are then used to create a dependence structure among observations on a unit. These may either be a form of serial dependence for longitudinal data or of uniform dependence within clusters. These are respectively analogous to the Kalman filter of state space models and to copulas, but they have the major advantage that they do not require any explicit integration. One useful outer distribution for constructing such multivariate distributions is the Pareto distribution. Certain special models based on it have previously been used in event history analysis, but those considered here have much wider application.     

我国能源消费的动态演变 ———基于时变参数的状态空间模型

采用多变量分析框架,将产业结构和技术进步与经济增长并列纳入研究体系分析能源消费的动态演变,构造了我国能源消费的可变系数状态空间模型,运用卡尔曼滤波方法对能源消费的收入弹性、产业结构弹性及生产率弹性的趋势变化进行估计。结果显示,经济增长和产业结构对能源消费有明显的促进作用,而全要素生产率与能源消费负相关,具有抑制能源消费的作用。从各弹性的长期变动趋势来看,能源的收入弹性有继续小幅提升的可能,产业结构弹性呈现明显的下降趋势,而全要素生产率对能源消费的影响程度日益加深。     

Exact filtering in exponential families:discrete time

An exact filter is an algorithm for calculating the a-posteriori distribution of the state ξ _n of a process, given observations η_t, …,η_nup to time n. We describe a method to determine an appropriate algorithm for processes, where the distributions involved are members of exponential families, The resulting algorithm consists essen tially of a prediction term, combined with an affine transformation depending on the chosen model.     

基于状态空间的贝叶斯跳跃厚尾金融随机波动模型研究

针对金融市场中跳跃特征的刻画问题,提出了贝叶斯跳跃厚尾随机波动模型。通过随机波动模型的结构分析和状态空间转换,设计了模型参数估计的MCMC算法,利用Kalman滤波和高斯模拟平滑方法估计模型的潜在波动,运用贝叶斯因子对随机波动类模型进行比较分析,并利用中国和美国的股市收益数据进行实证分析。研究结果表明:在刻画中、美两国股票市场的波动特征方面,跳跃厚尾随机波动模型要明显优于厚尾随机波动模型和标准随机波动模型,并且金融危机背景下的中国和美国股票市场都具有明显的波动持续性以及跳跃特征。     

部分信息下实物期权的定价和风险对冲

当前所有实物期权理论研究都是基于完全信息(full information)假设.本文则通过研究投资者在部分信息(partial information)下极大化无限期消费效用的最优投资消费问题,得出实物期权的消费效用无差别价格.通过控制系统的分离原理,运用Kalman滤波技术和随机控制方法,得到了CARA效用函数情形下实物期权的自由边界偏微分方程.利用有限差分法,解得实物期权的隐含价值及最优执行水平从而得到最优投资消费策略和效用函数的数值解.通过蒙特卡洛模拟,给出了投资者在完全信息和部分信息下的动态决策差异,并且通过比较两种信息水平下的投资者福利给出了信息价值的测算.     

State‐space model for proxy‐based millennial reconstruction

It is important to study historical temperature time series prior to the industrial revolution so that one can view the current global warming trend from a long‐term historical perspective. Because there are no instrumental records of such historical temperature data, climatologists have been interested in reconstructing historical temperatures using various proxy time series. In this paper, the authors examine a state‐space model approach for historical temperature reconstruction which not only makes use of the proxy data but also information on external forcings. A challenge in the implementation of this approach is the estimation of the parameters in the state‐space model. The authors developed two maximum likelihood methods for parameter estimation and studied the efficiency and asymptotic properties of the associated estimators through a combination of theoretical and numerical investigations. The Canadian Journal of Statistics 38: 488–505; 2010 © 2010 Crown in the right of Canada     

中国利率期限结构与货币政策联合建模的实证研究

本文采用卡尔曼滤波和极大似然函数估计方法对中国无套利利率期限结构与货币政策联合建模进行估计,实证结果显示通货膨胀目标值对利率期限结构的冲击是扩张性和持续性的,对于所有到期期限都是持续上升的;货币政策冲击对利率期限结构冲击的效应则是递减的,对利率期限结构曲线的斜率影响较显著;通货膨胀冲击对利率期限结构曲线的曲度影响较显著。模型样本外预测大大优于VAR模型。研究结果表明,本文构建模型一方面有助于对利率期限结构的预测,另一方面有助于央行制定前瞻有效的货币政策。     

An efficient algorithm for Harrison-Stevens forecasting using the multi-process multivariate dynamic linear model

This paper develops a computationally efficient algorithm for Harrison-Stevens forecasting in a multivariate time series which has correlated errors. The algorithm uses the observation vector one component at a time on the multiprocess multivariate dynamic linear model. This gives a computationally efficient, robust, quick adapting forecasting method for non stationary multivariate time series.     

Parameter Estimation in Credit Models Under Incomplete Information

We consider the filtering model of Frey and Schmidt (2012 Frey , R. , Schmidt , T. ( 2012 ). Pricing and hedging of credit derivatives via the innovations approach to nonlinear filtering . Fin. Stocha. 16 ( 1 ): 105 – 133 .[Crossref], [Web of Science ®] , [Google Scholar]) stated under the real probability measure and develop a method for estimating the parameters in this framework by using time-series data of CDS index spreads and classical maximum-likelihood algorithms. The estimation-approach incorporates the Kushner-Stratonovich SDE for the dynamics of the filtering probabilities. The convenient formula for the survival probability is a prerequisite for our estimation algorithm. We apply the developed maximum-likelihood algorithms on market data for historical CDS index spreads (iTraxx Europe Main Series) in order to estimate the parameters in the nonlinear filtering model for an exchangeable credit portfolio. Several such estimations are performed as well as accompanying statistical and numerical computations.