Are exchange rates interdependent? Evidence using wavelet analysis

We examine the co-movement in daily returns of USD–INR, EUR–INR, GBP–INR, and JPY–INR currency pair futures contracts traded on the National Stock Exchange of India (NSE) using the wavelet cohesion approach. This study contributes to the literature by examining the scantly studied area of co-movement in exchange rates and using the wavelet approach, which allows us to analyse time–frequency-wise co-movement of the time series. The empirical results indicate that the currency futures markets are nearly perfectly integrated in the long run (monthly, quarterly and biannual scales) offering little potential gains from international portfolio diversification. The discrepancies between currency futures markets are small and almost fade away within 3–6 months. Moreover, international currency diversification might offer relatively higher potential gains at intraweek, weekly, and fortnightly time horizons owing to lower correlations among the currencies under consideration. Finally, our multiple-wavelet correlation and cross-correlation analysis shows that GBP acts as a potential leader/follower across scales. The results of our analysis indicate the dynamic pattern of co-movement among the major currency futures contracts, which provides several implications for portfolio managers and international investors participating in the Indian market.     

小波变换在S模式二次雷达信号分析处理中的应用

为提高二次雷达（Secondary Surveillance Radar,SSR）信号分析处理能力,针 对傅里叶变换在时频域分析的局限性,利用小波信号奇异性检测特点,通过对S模式询 问、应答信号进行小波分解,计算第一层高频系数,得到信号脉冲持续时间,实现了信号报 头检测,并比较高频系数模极大值,提取出信号调制信息,实现了基于小波变换的二进制差 分相移键控（DPSK）和二进制振幅键控（ASK）解调,验证了小波变换技术分析处理二次雷 达信号的可行性。     

WA-BT-ELM耦合模型在黄土滑坡位移预测中的应用

黄土滑坡的变形演化过程往往受到多种因素的影响,呈现出非线性特征。基于小波分析函数(Wavelet Analysis,WA)、提升回归树(Boosting Regression Tree,BT),以及极限训练机(Extreme Learning Machine,ELM)方法,提出一种名为WA-BT-ELM的黄土滑坡位移预测新方法。该方法将非线性位移数据作为一时间序列,运用小波分析函数将监测点累积位移曲线分解为若干子小波;随后使用提升回归树对所有子小波进行重要度分析,剔除相关性不高的子小波以去掉冗杂信息;最后运用极限训练机,结合筛选得到的子小波对滑坡位移进行预测分析。基于该模型对甘肃省永靖县黑方台滑坡区的滑坡位移监测数据进行预测,得到了优于ANN,BPNN,SVM,ELM,以及WA-ELM预测模型的结果,故认为WA-BT-ELM模型是一种有效的黄土滑坡位移预测方法。     

基于小波分解和支持向量机的大坝位移监控模型

常规大坝安全监控统计模型未能分别针对监测序列值内系统信号和随机信号特点进行模拟,故预报精度存在一定的提升空间。基于小波分解技术,利用监测序列值信号频率特征分离出系统信号与随机信号,并结合逐步回归与支持向量机(SVM)对不同信号的处理优势,在引入网格寻优与交叉验证确定SVM敏感参数的基础上,提出了一种基于多元统计结合小波分解和支持向量机的大坝位移监控模型,同时编制了其相应的计算程序。工程算例表明,该模型较常规模型能够同时考虑监测序列中的系统信号和随机信号,并且具有较强的模型寻优能力和更高的预报精度,从而验证了所建模型的有效性,该方法亦可推广应用于高边坡及大坝其他预警指标的监控。     

低信噪比下LFM- PRBC信号参数的快速估计

为了解决目前算法中线性调频-伪码（LFM- PRBC）信号参数估计计算量较大的问题,提出了一种快速估计算法。该算法采用解线调与分数阶傅里叶变换（FRFT）进行参数的估计。首先对信号进行解线调估计出调频斜率的粗略值,然后由调频斜率确定旋转角,通过FRFT估计出码元宽度的粗略值。根据延时再进行解线调估计出调频斜率的精确值,再通过FRFT估计出码元宽度的精确值与起始频率。该算法不仅计算量较低,同时具有很高的估计精度与很强的抗噪性,仿真实验验证了该算法的有效性。     

大坝变形监测方法及数据处理

文章详细介绍了3种大坝变形监测的方法：极坐标法、前方交会法和GPS技术法,阐述了小波变换和灰色预测理论在实际工程变形数据分析中的应用。     

Barrier option pricing: a hybrid method approach

This paper adapts the hybrid method, a combination of the Laplace transformation and the finite-difference approach, to the pricing of barrier-style options. The hybrid method eliminates the time steps and provides a highly accurate and precise numerical solution that can be rapidly obtained. This method is superior to lattice methods when trying to solve barrier-style options. Previous studies have tried to solve barrier-style options; however, there have continually been several disadvantages. Very small time steps and stock node spaces are needed to avoid undesirable numerically induced oscillations in the solution of barrier option. In addition, all the intermediate option prices must be computed at each time step, even though one may be only interested in the terminal price of barrier-style complex options. The hybrid method may also solve more complex problems concerning barrier-style options with various boundary constraints such as options with a time-varying rebate. In order to demonstrate the accuracy and efficiency of the proposed scheme, we compare our algorithm with several well-known pricing formulas of barrier-type options. The numerical results show that the hybrid method is robust, and provides a highly accurate solution and fast convergence, regardless of whether or not the initial asset prices are close to the barrier.     

The density of the time to ruin for a Sparre Andersen process with Erlang arrivals and exponential claims

We derive expressions for the density of the time to ruin given that ruin occurs in a Sparre Andersen model in which individual claim amounts are exponentially distributed and inter-arrival times are distributed as Erlang(n,?β). We provide numerical illustrations of finite time ruin probabilities, as well as illustrating features of the density functions.     

Ruin probabilities in models with a Markov chain dependence structure

In this paper we derive explicit expressions for the probability of ruin in a renewal risk model with dependence among the increments (Z _k ) _k>0. We study the case where the dependence structure among (Z _k ) _k>0 is driven by a Markov chain with a transition kernel that can be described via ordinary differential equations with constant coefficients.     

Trend and cyclical decoupling: new estimates based on spectral causality tests and wavelet correlations

The issue of decoupling of emerging market economies (EMEs) (especially in the Asian region) from the developments in advanced economies has become a subject of lively debate in recent years. Basically, decoupling seems to comprise three sub-hypotheses: (i) growth spillovers from advanced countries to EMEs decreasing progressively in importance, (ii) business cycles in EMEs becoming less synchronized with those of the advanced world and (iii) strengthening of growth spillovers and cyclical synchronization among the EMEs as a group. The received literature fails to distinguish adequately between the trend and cyclical aspects of the decoupling relationship. We resort to two frequency domain methods (nonstationary spectral causality testing and wavelet correlations), which seem to offer a neat separation of trend and cyclical decoupling. Based on a sample of seven EMEs from the Asian region (including the two large EMEs – China and India), we uncover strong evidence favouring both trend and cyclical decoupling.