Structural learning with time-varying components: tracking the cross-section of financial time series

Summary.  When modelling multivariate financial data, the problem of structural learning is compounded by the fact that the covariance structure changes with time. Previous work has focused on modelling those changes by using multivariate stochastic volatility models. We present an alternative to these models that focuses instead on the latent graphical structure that is related to the precision matrix. We develop a graphical model for sequences of Gaussian random vectors when changes in the underlying graph occur at random times, and a new block of data is created with the addition or deletion of an edge. We show how a Bayesian hierarchical model incorporates both the uncertainty about that graph and the time variation thereof.     

我国油田技术效率与影响因素的实证研究

采用三要素超越对数式随机前沿生产函数对我国19个油田2001—2004年12个时期的技术效率进行了计量。详细讨论了要素的计量并提出了基于复合经济折旧的永续盘存资本计量方法。计量结果表明，我国油田近年来的技术效率平均仅为0．61。归纳了4大类影响技术效率的因素并进行了回归分析。分析结果表明，管理水平是目前影响我国油田技术效率的最重要的因素，其次是治理结构与技术人员比例，而勘探技术进步与成本技术进步对技术效率的促进作用非常微弱。最后，给出了一些提高油田技术效率的战略建议。     

金融波动研究的新进展及未来展望

金融波动的学术研究已经取得了突破性的进展,并且成为金融计量学中相对独立、颇具特色和最为活跃的研究领域之一.首先介绍了变结构金融波动模型研究取得的新进展,Copula函数在金融波动分析中的应用,Copula函数在金融波动相关性分析和金融传染分析中取得的新进展,以及基于高频数据的金融波动研究近年来取得的新进展,最后对金融波动研究领域中未来值得研究的方向进行了展望.     

RC串联电路的随机共振

研究了时间常数受非对称双值色噪声扰动的RC串联电路的随机共振现象。利用随机平均法和Shapiro-Loginov公式,得到了平均输出幅度增益的精确表达式。分析表明,平均输出幅度增益是噪声的强度、自相关率和非对称性参数的非单调函数,适当的噪声参数和系统参数可以使平均输出幅度增益取得最小值,输出幅度增益关于噪声非对称性和相关速率的函数的最小值不随信号频率的变化而变化。     

Bayesian inference for partially observed stochastic epidemics

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.     

Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model's numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for entry, establishment, and spread, to estimate the risks of invasion and their variation across a two-dimensional landscape for Sirex noctilio , a nonnative woodwasp recently detected in the United States and Canada. Here, we present a sensitivity analysis of the mapped risk estimates to variation in key model parameters. The tested parameter values were sampled from symmetric uniform distributions defined by a series of nested bounds (±5%, … , ±40%) around the parameters' initial values. The results suggest that the maximum annual spread distance, which governs long-distance dispersal, was by far the most sensitive parameter. At ±15% or larger variability bound increments for this parameter, there were noteworthy shifts in map risk values, but no other parameter had a major effect, even at wider bounds of variation. The methodology presented here is generic and can be used to assess the impact of uncertainties on the stability of pest risk maps as well as to identify geographic areas for which management decisions can be made confidently, regardless of uncertainty.     

QFII制度下中国A，B股市场波动性特征比较

文章采用GARCH模型，从实证的角度对比分析QFII制度实施以后沪深A，B股市场的波动性特征。结果发现，引入QFII制度后，沪深A股指数收益率的波动性明显低于B股指数的波动性。     

沪深A股波动协同持续性研究

文章从单变量的协整思想出发,根据Bollerslev、Engle(1993)协同持续,采用变量GARCH的方法来研究我国沪深股市A股的波动协同持续性特征,实证研究发现在样本期内,我国沪市A股和深市A股之间确实存在波动协同持续性特征,说明A股市场联系性较强。     

Multivariate Stochastic Volatility: A Review

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed.     

Communicating Asset Risk: How Name Recognition and the Format of Historic Volatility Information Affect Risk Perception and Investment Decisions

An experiment examined how the type and presentation format of information about investment options affected investors' expectations about asset risk, returns, and volatility and how these expectations related to asset choice. Respondents were provided with the names of 16 domestic and foreign investment options, with 10-year historical return information for these options, or with both. Historical returns were presented either as a bar graph of returns per year or as a continuous density distribution. Provision of asset names allowed for the investigation of the mechanisms underlying the home bias in investment choice and other asset familiarity effects. Respondents provided their expectations of future returns, volatility, and expected risk, and indicated the options they would choose to invest in. Expected returns closely resembled historical expected values. Risk and volatility perceptions both varied significantly as a function of the type and format of information, but in different ways. Expected returns and perceived risk, not predicted volatility, predicted portfolio decisions.