The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models

We study the effect of parameter and model uncertainty on the left‐tail of predictive densities and in particular on VaR forecasts. To this end, we evaluate the predictive performance of several GARCH‐type models estimated via Bayesian and maximum likelihood techniques. In addition to individual models, several combination methods are considered, such as Bayesian model averaging and (censored) optimal pooling for linear, log or beta linear pools. Daily returns for a set of stock market indexes are predicted over about 13 years from the early 2000s. We find that Bayesian predictive densities improve the VaR backtest at the 1% risk level for single models and for linear and log pools. We also find that the robust VaR backtest exhibited by linear and log pools is better than the backtest of single models at the 5% risk level. Finally, the equally weighted linear pool of Bayesian predictives tends to be the best VaR forecaster in a set of 42 forecasting techniques.     

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

Virtual reality mobility model for wireless ad hoc networks

For wireless ad hoc networks simulation, node＇s mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node＇s moving path. Obstacles also affect node＇s signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. Simulation results show that the model has a significant impact on the performance of protocols.     

多阶段任务系统任务持续能力仿真模型研究

多阶段任务系统(PMS)是一种典型的复杂系统,它包括多个在时间上连续且无相互重叠的阶段任务,执行作战与使用任务的武器装备多数属于这种复杂系统。分析了PMS及任务可靠度、可信度和任务效能等任务持续能力评价参数。结合实际装备系统大都属于可用马尔可夫过程进行描述的可修复系统的特点,为简化模型复杂程度提出了一些合理的假设条件。在此基础上,结合多阶段任务系统自身特点,通过分析多阶段任务系统任务持续能力建模仿真步骤,多阶段任务系统任务效能仿真方法,建立了基于Petri网的多阶段任务系统任务效能多层仿真模型。最后结合常见的"靶场打靶"任务进行了实例验证,并对仿真结果进行了分析。     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

复杂网络确定性模型研究的最新进展

复杂网络的确定性模型在复杂网络建模领域起着不可或缺的作用。按照复杂网络经典模型发展的先后次序,依次对随机图、小世界网络、无标度网络以及加权网络确定性模型的研究现状进行较全面的综述,重点介绍了相关模型的构造及其主要性质,并对复杂网络确定性模型未来的部分发展方向进行了展望。     

The neurotrophic receptor TrkB: a drug target in anti-cancer therapy?

Increasing evidence implies altered signaling through the neurotrophic receptor tyrosine kinase TrkB in promoting tumor formation and metastasis. TrkB, sometimes in conjunction with its primary ligand BDNF, is often overexpressed in a variety of human cancers, ranging from neuroblastomas to pancreatic ductal adenocarcinomas, in which it may allow tumor expansion and contribute to resistance to anti-tumor agents. In vitro, TrkB acts as a potent suppressor of anoikis (detachment-induced apoptosis), which is associated with the acquisition of an aggressive tumorigenic and metastatic phenotype in vivo. In view of its predicted contribution to tumorigenicity and metastasis in humans, TrkB corresponds to a potential drug target, and preclinical models have already been established. The encouraging results of pharmacological Trk inhibitors in tumor xenograft models suggest that TrkB inhibition may represent a promising novel anti-tumor therapeutic strategy. This hypothesis is currently being evaluated in clinical trials. Here, we will discuss the latest developments on TrkB in these contexts as well as highlight some critical questions that remain to be addressed for evaluating TrkB as a therapeutic target in cancer. Received 12 October 2005; received after revision 19 December 2005; accepted 11 January 2006     

DBD大气压等离子体的数值模拟研究进展

介绍了大气压介质阻挡放电(DBD)的一般结构及其特性,并说明对其数值模拟的必要和意义。同时在介绍了一些常用的模拟模型基础上,重点对流体模型和粒子云—蒙太卡洛模拟加以描述。最后结合模拟研究的历史和当前进展,展望了DBD大气压等离子体的数值模拟研究的难点和前景。