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.     

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.     

基于伪谱方法的自适应鲁棒实时再入制导律研究

为了提高飞行器再入制导的鲁棒性和自适应性,将再入弹道跟踪问题转化为再入弹道状态调节问题,得到了一个LTV系统最优控制问题.在此基础上,利用基于伪谱算法的最优反馈控制算法,设计了一种便于在线实现的自适应鲁棒再入制导律.仿真结果表明,这种再入制导律对于再入点误差不敏感,具有良好的鲁棒陛.在气动参数模型存在较大误差的情况下,依然能够取得较高的再入制导精度.它不需要显示增益调度和积分,并且在不同情况下控制结构和参数无需改变.     

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.     

月径流序列的多层递阶预报研究

月径流序列是一类具有周期变化的非平稳时间序列．本文根据其特点,建立了多层递阶预报模型,文中对此类非平稳时间序列的建模及预报方法进行了深入研究     

关于G/G/1排队模型中排队长度的积分

考虑了一个具有中度正则变化服务时间的G/G/1模型.假设Q(t)是排队长度,则在忙期[0,l]上,Q(t)下方所扫过的面积也具有中度正则变化的性质.     

力学中一类变系数微分方程可调参数模型解法

结合一种非均匀控制参数，提出了一种变系数微分方程的可调整参数模型解法，可以很方便地处理由于物理上，几何上的非均匀，非线性而导致寂的变系数微分方程，应用这种模型可以用非常少的单元得到较满意的数值结果。     

GLMS系列算法和时变信号模型的自适应处理

提出了具有指数数据窗的GLMS算法和近似GLMS算法.分析和计算证明,两算法不仅具有较高的起始收敛速度,而且还有较强的跟踪能力和小的失调量,适用于时变和非时变信号模型的自适应处理.     

关于Von Mises条件的注记

ＶｏｎＭｉｓｅｓ（１９３６）在一定条件下给出了独立同分布随机变量序列｛Ｘ＿ｎ｝之分布函数Ｆ（ｘ）属于三大吸引场的充分条件。受Ｇａｌａｍｂｏｓ（１９８７）文章的启发，在一定限制下，使ＶｏｎＭｉｓｅｓ条件也成为Ｆ∈Ｄ（Φ＿ａ）及Ｅ∈Ｄ（Φ＿ａ）的充要条件。