p-moment stability of stochastic impulsive differential equations and its application in impulsive control

The exponential p-moment stability of stochastic impulsive differential equations is addressed.A new theorem to ensure the p-moment stability is established for the trivial solution of the stochastic impulsive differential system.As an application of the theorem proposed,the problem of controlling chaos of Lorenz system which is excited by parameter white-noise excitation is considered using impulsive control method.Finally,numerical simulation results are given to verify the feasibility of our approach.     

Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis

Interest in online auctions has been growing in recent years. There is an extensive literature on this topic, whereas modeling online auction price process constitutes one of the most active research areas. Most of the research, however, only focuses on modeling price curves, ignoring the bidding process. In this paper, a semiparametric regression model is proposed to model the online auction process. This model captures two main features of online auction data: changing arrival rates of bidding processes and changing dynamics of prices. A new inference procedure using B‐splines is also established for parameter estimation. The proposed model is used to forecast the price of an online auction. The advantage of this proposed approach is that the price can be forecast dynamically and the prediction can be updated according to newly arriving information. The model is applied to Xbox data with satisfactory forecasting properties. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting the price of crude oil via convenience yield predictions

The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield, which can be derived from the cost‐of‐carry relationship. In a recursive out‐of‐sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction‐of‐change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis‐à‐vis the random‐walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change. Copyright © 2007 John Wiley & Sons, Ltd.     

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.     

A detailed look at crude oil price volatility prediction using macroeconomic variables

We investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out-of-sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables related to crude oil production, economic uncertainty and variables that either describe the current stance or provide information about the future state of the economy forecast crude oil price volatility at the population level 1 month ahead. On the other hand, evidence of finite-sample predictability is very weak. A detailed examination of our out-of-sample results using the fluctuation test suggests that this is because relative predictive performance changes drastically over the out-of-sample period. The predictive power associated with the more successful macroeconomic variables concentrates around the Great Recession until 2015. They also generate the strongest signal of a decrease in the price of crude oil towards the end of 2008.     

Step-ahead spot price densities using daily synchronously reported prices and wind forecasts

This paper uses non-linear methodologies to follow the synchronously reported relationship between the Nordic/Baltic electric daily spot auction prices and geographical relevant wind forecasts in MWh from early 2013 to 2020. It is a well-known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11 a.m. The main objective is therefore to establish conditional and marginal step ahead spot price density forecast using a stochastic representation of the lagged, synchronously reported and stationary spot price and wind forecast movements. Using an upward expansion path applying the Schwarz (Bayesian information criterion [BIC]) criterion and a battery of residual test statistics, an optimal maximum likelihood process density is suggested. The optimal specification reports a significant negative covariance between the daily price and wind forecast movements. Conditional on bivariate lags from the SNP information and using the known market information for wind forecast movements at t₁, the paper establishes one-step-ahead bivariate and marginal day-ahead spot price movement densities. The result shows that wind forecasts significantly influence the synchronously reported spot price densities (means and volatilities). The paper reports day-ahead bivariate and marginal densities for spot price movements conditional on several very plausible price and wind forecast movements. The paper suggests day-ahead spot price predictions from conditional and synchronously reported wind forecasts movements. The information should increase market participants spot market insight and consequently make spot price predictions more accurate and the confidence interval considerably narrower.     

基于随机时间自动机和统计模型检验技术的无线传感网络协议建模与分析

近年来,传感器技术得到了长足而有效的提升,无线传感网络（WSN）以其开放、动态的特征获得了极大的关注,并成为了互联网计算的一个重要组成．WSN系统行为复杂,经常面临信息丢失、节点动态变化等不确定因素,且网络中的节点一旦部署将很难更改、维护．因此,为了保证相关应用的正常工作,在系统设计阶段对WSN中的底层协议进行质量保障就成为了一项非常重要的研究问题．系统设计人员不仅需要保证协议功能上的正确性,还应该评估协议在目标工作环境下的性能,以保证其可以胜任相应的工作需求．针对以上问题,本文提出了一种基于随机时间自动机和统计模型检验技术的WSN协议建模、分析和评估途径．在建模阶段,首先将采用时间自动机对协议在理想环境下的基本业务流程进行建模．考虑到WSN系统实际工作中会遇到的各种不确定性因素,将用带权分枝来对模型进行扩展,生成协议的随机时间自动机．在验证阶段,首先采用经典模型检验技术,在理想时间自动机上检验相关功能性质,保证协议工作逻辑的正确性．为评估协议在不同条件下的具体性能,则在随机时间自动机上用统计模型检验技术对其进行数值分析,以进行参数配置、性能预测、协议比较等工作．为展示该途径的可用性及其技术细节,本文对两种著名的WSN时间同步协议,TPSN和FTSP分别进行了完整的建模与评估．     

Kinetic analysis of lactate dehydrogenase using integrated rate equations

The reaction catalyzed by lactate dehydrogenase was analyzed under fully second-order conditions using integrated rate equations. A two-step regression analysis was utilized to fit twenty-one progress curves repeated in sextuplicate to the general mechanism second-order integrated rate equation with additional terms for substrate inhibition. The fitting error was less than one percent. The resulting kinetic constants support a ternary complex mechanism; in no case were constants supporting another mechanism predicted. The inhibition constant for oxamate was also determined.     

A perturbation-incremental scheme for studying Hopf bifurcation in delayed differential systems

A new method,called perturbation-incremental scheme (PIS),is presented to investigate the periodic solution derived from Hopf bifurcation due to time delay in a system of first-order delayed differential equations.The method is summarized as three steps,namely linear analysis at critical value,perturbation and increment for continuation.The PIS can bypass and avoid the tedious calculation of the center manifold reduction (CMR) and normal form.Meanwhile,the PIS not only inherits the advantages of the method ...     

有色噪声下随机时滞传染病模型的稳定性

本文运用O-U过程刻画环境变化性,在Edoardo Beretta基础上构造了有色噪声影响下的随机时滞的传染病模型。运用一般Lyapunonv方法研究了有色噪声对该系统的影响并得到系统正平衡点保持稳定的充分条件。最后通过对比发现随机扰动对系统稳定性影响仅仅与其随机过程的方差有关。     

Time series forecasting using functional partial least square regression with stochastic volatility,GARCH, and exponential smoothing

We propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.