The interrelationship between order flow,exchange rate,and the role of American economic news

The aim of this study is to investigate the comovement and interconnection between US economic news in seven different categories and the exchange rate of the US dollar against the currencies of seven developed countries, with the corresponding order flows. Our empirical investigation is based on using the Spearman correlation method to analyze the correlation between the economic news and exchange rate using order flows before, during, and after the financial news announcement. We use wavelet transform analysis to assess the comovement in the time–frequency domains. The bivariate outcomes demonstrate that changes in economic news impact price changes for only a short time, or even no time at all. Moreover, for longer time periods, the order flow is shown to have stronger coherency than economic news and a steady comovement against the exchange rate. Wavelet coherency reveals that the lead-lag effect of order flow changes on price changes starts in the medium frequency bands to low-frequency bands during the entire sample period. In addition, economic news weakens the correlation between exchange rate and order flow in the short run, which means that the combination of economic news and order flow can achieve a higher degree of consistency of exchange rate. The implication of this study is that macro policy makers, import and export enterprises, foreign exchange investors and exchange fund managers can predict the future exchange rate based on the order flow and choose hedging measures under different objectives.     

Practical estimation of multivariate densities using wavelet methods

This paper describes a practical method for estimating multivariate densities using wavelets. As in kernel methods, wavelet methods depend on two types of parameters. On the one hand we have a functional parameter: the wavelet Ø (comparable to the kernel K ) and on the other hand we have a smoothing parameter: the resolution index (comparable to the bandwidth h ). Classically, we determine the resolution index with a cross-validation method. The advantage of wavelet methods compared to kernel methods is that we have a technique for choosing the wavelet Ø among a fixed family. Moreover, the wavelets method simplifies significantly both the theoretical and the practical computations.     

Description, modelling and forecasting of data with optimal wavelets

Cascade processes have been used to model many different self-similar systems, as they are able to accurately describe most of their global statistical properties. The so-called optimal wavelet basis allows to achieve a geometrical representation of the cascade process-named microcanonical cascade- that describes the behavior of local quantities and thus it helps to reveal the underlying dynamics of the system. In this context, we study the benefits of using the optimal wavelet in contrast to other wavelets when used to define cascade variables, and we provide an optimality degree estimator that is appropriate to determine the closest-to-optimal wavelet in real data. Particularizing the analysis to stock market series, we show that they can be represented by microcanonical cascades in both the logarithm of the price and the volatility. Also, as a promising application in forecasting, we derive the distribution of the value of next point of the series conditioned to the knowledge of past points and the cascade structure, i.e., the stochastic kernel of the cascade process.     

基于小波变换的图像融合方法及其性能评价

近年来,图像融合已成为图像理解和计算机视觉领域一项重要的新技术。把小波变换技术应用到图像融合之中是该研究领域的重大突破。文章以小波变换为工具,描述了基于小波变换的多传感器图像融合算法的实现,提出了多种融合规则和融合算子,并对融合结果进行了深入分析和性能评价。     

Estimators of long-memory: Fourier versus wavelets

Semi-parametric estimation methods of the long-memory exponent of a time series have been studied in several papers, some applied, others theoretical, some using Fourier methods, others using a wavelet-based technique. In this paper, we compare the Fourier and wavelet approaches to the local regression method and to the local Whittle method. We provide an overview of these methods, describe what has been done and indicate the available results and the conditions under which they hold. We discuss their relative strengths and weaknesses both from a practical and a theoretical perspective. We also include a simulation-based comparison. The software written to support this work is available on demand and we illustrate its use at the end of the paper.     

Stablecoins as diversifiers,hedges and safe havens: A quantile coherency approach

This study investigates the diversifier, hedge and safe haven properties of stablecoins against various financial assets including cryptocurrencies such as Bitcoin, Ether, XRP and stock market indices. Using quantile coherency we show that stablecoins included in the study act as weak hedges in normal conditions and weak safe havens when considering moments of market turmoil and there is little evidence to support the existence of any contagion effects between the cryptocurrency and stablecoin markets. Aforementioned results are not significantly influenced by the choice of investment horizon. We further evaluate the implications of those results for the question of whether stablecoins are in fact stable.     

基于小波变换的图像融合方法及其性能评价

近年来,图像融合已成为图像理解和计算机视觉领域一项重要的新技术。把小波变换技术应用到图像融合之中是该研究领域的重大突破。文章以小波变换为工具,描述了基于小波变换的多传感器图像融合算法的实现,提出了多种融合规则和融合算子,并对融合结果进行了深入分析和性能评价。     

Looking for a safe haven against American stocks during COVID-19 pandemic

This article aims to find the best safe-haven for stock investors in the American market since the COVID-19 pandemic outbreak. The research period covers March 2020–May 2022. Among the possible alternatives, we analyse the traditional ones: US bonds, gold, and silver, as well as the new ones: stable DeFi and CeFi coins, and most popular cryptocurrencies: Bitcoin and Ether. We study quantile coherency between S&P 500 and each asset and the respective conditional correlation. We show that the safe-haven properties of the assets varied over time and that centralized stablecoins could have been used as safe-haven against American stocks during the pandemics.     

Market volatility and illiquidity during the COVID-19 outbreak: Evidence from the Saudi stock exchange through the wavelet coherence approaches

The aim of this paper is to examine the explanatory power of realized volatility on the illiquidity in Saudi stock market during the COVID-19 outbreak. To achieve this objective, we consider the Wavelet Coherence approaches as empirical tools to investigate the combined effect of realized volatility and COVID-19 counts on the market illiquidity across frequencies and over time space by taking in account the number of infected cases in Saudi Arabia and over the World, and the number of death cases in Saudi Arabia as well as over the World. Our study reaches two main findings. First, the preliminary results reported by the ARDL bound test as a benchmark model showed significant long-run and short-run effects of the market volatility on illiquidity in contemporaneous and lagged manner. Second, the wavelet coherence analysis tools exhibited important results: (i) the wavelet coherency between illiquidity ratio and realized volatility in Saudi Arabia appear highly pronounced over all time horizons. (ii) PWC plots showed a significant mutual effect between liquidity risk and realized volatility when eliminating the effect of local COVID-19 cases. (iii) MWC plots highlighted that the response of the market illiquidity index to both the amplification in confirmed local cases (resp. international confirmed cases) and the stock market volatility appear significant in the short and middle horizons.     

Blockwise bootstrap wavelet in nonparametric regression model with weakly dependent processes

In this paper, we suggest a blockwise bootstrap wavelet to estimate the regression function in the nonparametric regression models with weakly dependent processes for both designs of fixed and random. We obtain the asymptotic orders of the biases and variances of the estimators and establish the asymptotic normality for a modified version of the estimators. We also introduce a principle to select the length of data block. These results show that the blockwise bootstrap wavelet is valid for general weakly dependent processes such as α-mixing, φ-mixing and ρ-mixing random variables.     

中国股市β系数的多尺度特性分析

本文提出一种基于小波方差和小波协方差的β系数估计方法,并通过小波方差和小波协方差的多尺度分解估计出不同尺度上的风险系数,用该方法对中国证券A股市场分行业及投资组合的β系数进行了多尺度估计分析。实证结果表明,我国股市具有复杂的多尺度波动的特征,不同时间尺度上证券市场所表现出的风险不一样,短期投资的风险主要表现在高频波动,投资者应当考虑低尺度下的β系数,而长期投资风险主要表现为低频波动,应当考虑大尺度下的β系数。     

A GUIDE TO WAVELETS FOR ECONOMISTS*

Abstract Wavelet analysis, although used extensively in disciplines such as signal processing, engineering, medical sciences, physics and astronomy, has not fully entered the economics discipline yet. In this survey article, wavelet analysis is introduced in an intuitive manner, and the existing economics and finance literature that utilizes wavelets is surveyed and explored. Extensive examples of exploratory wavelet analysis are given, most using Canadian, US and Finnish industrial production data. Finally, potential and possible future applications for wavelet analysis in economics are discussed.     

小波神经网络对沪深A300的分析和预测

针对金融时间序列非平稳性、非线性的特点,本文采用小波分析与人工神经网络相结合的方法,对沪深A300收盘价进行分析和预测。结果表明,小波神经网络有较强的预测能力,能达到预期效果。为了验证该方法的预测能力,进一步将时间序列数据多步分段,全方位地进行预测,并与小波-ARIMA模型、BP神经网络预测方法进行比较,体现了小波神经网络的预测优势。     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

小波的信号突变点检测在车流量检测器中的应用

为了提高车流量检测器的精度,通过现场采样大量实验数据,建立车辆通过感应线圈时频率变化曲线的数学模型。采用小波变换对频率变化曲线的突变点进行分析,得到车流量检测的新方法。这种方法不但能有效的提高车流量的检测精度,而且为进一步通过车流量检测器实现对各种车型进行预分类提供了新的思路,具有相当的研究价值。分析过程中还对利用小波变换分析信号突变点的理论依据进行了描述,并介绍了小波基的选择方法。     

基于改进的小波变换和模糊核聚类的纹理分割

提出了一种基于改进的小波变换和模糊核聚类的纹理分割方法。该方法首先用改进的离散小波变换进行纹理特征提取。然后用模糊核聚类方法对特征空间的每个像素进行聚类以实现对纹理的分割。实验结果表明所提算法有很好的分割结果。     

基于小波变换的GPS变形监测数据处理方法研究

工程实测变形信号不可避免的受到噪声的污染,为了提取真实的变形信号,要对实测数据进行降噪处理。结合武汉某大桥GPS实测变形数据介绍了小波变换阈值降噪过程,分别采用不同的小波函数和不同阈值模型进行降噪处理。根据信噪比、中误差两种降噪质量评价指标的定量比较,得出用db6小波和heursure阈值在软阈值量化作用下降噪效果较其他小波函数和阈值模型效果好。     

Co-movement of oil and stock prices in the GCC region: A wavelet analysis

This paper examines the short term and long term dependencies between stock market returns and OPEC basket oil returns for the six Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and two non-oil producing countries in the region (Egypt and Jordan), over the period 2002–2011. We utilize the wavelet coherency methodology in our empirical analyses. The empirical evidence indicates lack of market dependencies in the short term in these countries, indicating that oil and stock returns are not strongly linked in this interval. However, we show that oil returns and the stock markets returns co-move over the long term. The results also suggest that the long term dependencies are much stronger for OPEC oil returns and Jordan stock market returns relative to OPEC oil returns and Egypt stock market returns, implying a variation in the dependencies between oil prices and stock markets across countries. We further note an increasing strength in the market dependencies after 2007, signifying enhanced diversification benefit for investors in the short term relative to the long term.     

基于小波分析与数学形态学融合的降噪算法研究

本文建立了实际工程应用中信号的仿真模型和干扰模型,运用小波变换和数学形态学相融合的方法进行降噪,并将该方法与小波阈值方法、小波模极大值法、数学形态学方法的仿真结果进行对比分析,仿真分析结果发现小波-形态融合方法能够有效的消除噪声且能较好地保留原信号的形态特征。     

浅析小波分析在电力系统谐波治理中的应用

文章深入分析了小波分析理论,介绍了谐波的产生及危害,并通过仿真分析探讨在电力系统谐波治理中引入小波多分辨率分析的效果。