Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

5G-TSN系统下的高精度时间同步

第五代移动通信系统(the fifth generation communication system, 5G)与时间敏感网络(time sensitive networking, TSN)集成的5G-TSN系统需要实现高精度的时间同步。为此，提出一种多波束联合的定时同步算法提高同步信号检测成功率，实现高精度5G-TSN时间同步。首先，根据多个波束间的时域关系计算波束间同步信号块的互相关的组合，求出期望作为门限值；然后，利用超过门限值的若干波束进行互相关叠加峰值计算最大峰值对应的时域索引，并完成主同步信号定时同步；最后，利用时频域结合地方式对传播时延进行估计以减少同步误差。仿真结果表明，采用基于多波束联合的定时同步算法，相比传统算法，相关峰值更加显著，能有效地提高定时同步成功率。     

一类具有时滞的松材线虫非线性传播模型的稳定性分析和Hopf分支

建立带有松褐天牛羽化时滞的微分方程模型,研究松褐天牛的羽化时间对松材线虫病在松树间传播的影响,得到了平衡状态稳定时滞的临界条件和Hopf分支.最后用Matlab对模型进行了数值模拟以验证数学结果.     

用于异质用户出行管理的可交易许可证研究

针对公共交通与私家车出行方式并行的双模式交通系统,在考虑不同用户的时间价值情况下,通过引进可交易的道路许可证政策,建立了均衡出行交通模型.研究发现,许可证政策将拥挤产生的交通成本内部化,在缓解交通拥堵的同时,通过经济手段,降低了系统总成本.进一步地,本文证明如果汽车比公交快,那么时间价值较高的人会选择私家车出行;反之,如果公交比汽车快,时间价值相对较高的有车族会选择公交出行.通过与OD收费进行比较,我们得到在收费为正的情况下,许可证政策可以得到与基于OD收费相同的效果.最后,算例结果表明,许可证政策使得部分人选择公交出行的成本更低,达到鼓励公交出行的目的;甚至在许可证发放数量少于临界数量的情况下,选择公交出行的人会获得正收益.     

基于离散时间风险模型下的亏损破产概率的研究

新定义离散时间风险模型下的亏损破产概率为初始盈余u,亏损额度不大于y的破产概率。利用离散时间风险模型下的终时破产概率的计算规律,得到初始盈余水平在不同条件下的亏损破产概率的具体表达形式,并且数值模拟了一定条件下不同参数取值对亏损破产概率的影响情况,数据表明当亏损边界固定时,随着初始盈余水平的增加,亏损破产概率水平逐渐减小;当初始盈余水平固定时,随着亏损边界的增加,亏损破产概率水平逐渐增多。     

近临界紧邻随机游动局部时的重整化极限

Lamperti于1961年证明了在适当条件下, 半直线上一类近临界的紧邻随机游动经过重整化会弱收敛到布朗运动. 考虑过程局部时重整化的极限问题, 运用随机游动的内蕴分枝结构以及非时齐分枝过程重整化极限的结果, 证明了其局部时经过适当的重整化会收敛到布朗运动的局部时.      

时序法在焊缝损伤诊断方面的应用

初步探讨如何采取时间序列分析的方法对焊接结构、焊缝的缺陷进行诊断,并获得能够反映系统状态的参数,从而判断出焊缝质量的不同情况.     

基于广义出行成本的出行方案优化

为了使出行成本更加符合出行者的认知规律,运用结构方程建模方法模拟出行成本,个体特征及出行决策之间的影响关系.引入敏感系数调整服务属性在出行成本中的贡献率,利用出行意愿调查数据对敏感系数进行标定.考虑出行者对抵达时刻的选择偏好,假设行程时间服从正态分布条件下,对最佳出发时刻及期望出行成本进行寻优求解,结果表明它取决于期望到达时刻,行程时间的期望值及方差,出行者对早到损失及晚到延误损失时间成本的相对大小.研究成果可为制定个性化及最优化出行决策方案提供依据.     

分数阶中立型时滞微分方程解的存在性及指数估计

近年来,随着分数阶微分方程在众多领域的广泛应用,其理论研究也引起了国内外学者的关注.论文研究分数阶中立型时滞微分方程在解存在的前提下其解的指数估计.首先,由分步法讨论分数阶中立型时滞微分方程的解的存在唯一的条件;然后,在解存在的前提下,利用Gronwall不等式,给出分数阶中立型时滞微分方程解的指数估计.