基于跳扩散模型的石油价格长期趋势分析

分析了国际石油市场1986至2012年周价格形成机制的长期演变趋势.在讨论均衡理论基础上,以长期市场供求关系解释了国际油价长期波动现象.基于跳扩散模型拟合石油价格动态过程,利用结构变点检验和累积量估计方法进行了实证研究.历史数据分析表明石油价格具有高波动性、高强度跳跃性和上升漂移特征.此外,模型预测即使当前大幅增加石油投资,未来几年内石油价格变化仍会处于一种高频跳跃的上行阶段.     

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.     

基于自抗扰控制的不同市场环境库存优化

生产库存问题中，由于市场需求的时变性以及不确定性，很难通过精确的理论模型选择调控策略。自抗扰控制（ADRC）技术是工程实践中基于误差反馈的控制方法，它对非线性、大时滞、强不确定性控制对象具有很强的鲁棒性和适应性。基于ADRC研究库存模型在不同市场环境下的生产控制策略。结果表明：当销售函数是时间的确定性函数时，ADRC能够给出与最优解近似一致的企业生产控制策略；当市场销售存在不确定性扰动时，ADRC能够根据实际库存状态和目标状态的误差给出令人满意的生产策略，化解不确定市场环境对企业的冲击。研究表明，在解决实际库存控制过程中，ADRC更具操作性和可控性，同时可为经济管理领域不确定最优控制问题提供一种新的分析思路。     

Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

时变参数模型在建筑物变形分析中的应用

对传统GM(1,1)沉降预测模型进行分析后,发现参数采用固定值与实际情况不符,会影响预测精度.为弥补这一不足,对传统灰色模型进行改进,假定参数是时间的连续函数,并对之做了拟合,提出含时变参数的GM(1,1)预测模型.实例证明此模型比传统GM(1,1)模型精度更高.     

基于牵制控制的变时滞复杂网络自适应同步

在牵制控制的基础上,文章探讨了节点与耦合均含变时滞复杂网络的自适应同步问题.首先,利用非线性耦合常微分方程描述了一般复杂网络的动态模型,同时给出了必要的定义、假设和引理.然后,基于李雅普诺夫稳定性理论和自适应控制原理,设计了简单适当的自适应控制器,让其通过对部分节点进行牵制控制来实现整个网络的同步,并且给出网络渐近同步的充分条件.最后,通过数值实验结果很好地验证了理论的可行性与有效性.     

带有加性时变时滞的级联切换系统的指数稳定性

讨论了带有加性时滞的级联切换系统的指数稳定性.将网络控制系统中的网络诱导时延和数据丢包问题归结为切换系统的各子系统中的两个加性时滞,并构造相应的Lyapunov-Krasovskii泛函.设计满足平均驻留时间条件的切换规则,并结合Jensen积分不等式,以线性矩阵不等式的形式给出确保带有加性时滞的级联切换系统指数稳定的充分条件,所得结果具有更小的保守性.最后用数值例子验证了该方法的有效性.     

BAM神经网络的稳定性分析

本文研究了BAM神经网络的稳定性问题,通过构造新的Lyapunov泛函,给出了此类系统平衡点渐近稳定的充分条件,并举例说明了结果的有效性和保守性.该充分条件对丰富和完善了神经网络的稳定性理论体系,具有积极的意义.