Sovereign and bank dependence in the eurozone: A multi-scale approach using wavelet-network analysis

This study establishes time–frequency networks of sovereign and bank contagion in the eurozone over the period 2009–2021. By applying discrete wavelet transformation, daily CDS premia of sovereigns and systemically important banks are decomposed into multi-horizon components to specify directed and dependence-weighted networks. Dynamic analysis shows that the network connectivity and the strength of the dependencies are significantly lower after the introduction of the European Banking Union in 2014. While the strength effect is pronounced across all time horizons, the network connectivity only reduces in the short and medium run. This provides evidence that the new regulatory framework promotes financial stability but is more effective in the short and medium horizons. The consideration of the COVID-19 pandemic as a real-life stress test confirms these findings as the strength of the dependencies keeps at significantly lower levels.     

智能信息处理方法在股市研究中的应用

本主要介绍了智能信息处理方法在股市研究中的应用，着重介绍了基于模糊数学方法的政策因素综合评价研究、神经网络模型在股票价格分析中的应用和小波包在股票价格预测中的应用。     

Changing patterns of Asian currencies’ co-movement with the US dollar and the Chinese renminbi: Evidence from a wavelet multiresolution analysis

We extend the Frankel–Wei approach by using wavelet analysis to evaluate the relative importance of the dollar and the renminbi as anchor currencies at different time scales. We find that Asian currencies’ co-movement with the dollar weakened after the global financial crisis, while that with the renminbi strengthened particularly after China introduced a new exchange rate management system in 2015. The evidence suggests that emerging Asian economies have recently attached more importance to the renminbi as an anchor in exchange rate management.     

Dependent relationships between Chinese commodity markets and the international financial market: Evidence from quantile time-frequency analysis

In this article, the quantile time–frequency method is utilized to study the dependence of Chinese commodities on the international financial market. The impacts of risk management and diversification benefits of different portfolios are examined by calculating the reduction in downside risk. Moreover, we estimate and compare Sharpe Ratios (SRs) and Generalized Sharpe Ratios (GSRs) based on the frequencies of the investigated portfolios. Our empirical results reveal a strong asymmetric response from Chinese commodity markets. Specifically, we find that gold is a safe-haven asset, and due to negative correlations found at lower quantiles in medium and long term, an increase in the USD index damages bull commodity markets but boosts bear conditions under long-term investments, and negative (positive) tail correlations with interest rates (IRs) in bull (bear) markets are observed. It is proven that WTI can decrease short-run risks while USD and GOLD are more efficient in the diversification of downside risk. Adding international commodities may not improve the returns of Chinese commodities at given risk levels in the short and medium term through SRs and GSRs. In brief, investors should consider these dependence structures and modes of risk management in terms of time and frequency.     

Automatic stabilizers in the Japanese tax system

When studying the role of taxes as an automatic stabilizer, excluding the effects of discretionary tax reform is troublesome. A fruitful approach to identifying tax movements over business cycles would be to utilize cyclical information. From this perspective, we exploit wavelets in order to characterize the automatic response of taxes to output at business cycle frequencies in the postwar Japanese economy. We find the presence of automatic stabilizers in the Japanese tax system under the influence of relatively high output volatility but the absence of such stabilizers during the period of the “Great Moderation” in Japan in the 1980s.     

基于小波神经网络的高阶CAPM实证研究

本文在传统CAPM的基础上,引入了一个高阶的CAPM。借助小波神经网络在非线性函数逼近方面的优势,使用上海证券交易所股票数据分别对二阶至四阶CAPM进行了实证分析。最终的研究结果表明:就上海股市而言,12只大盘股组合已经能够有效分散非系统风险,而12只小盘股不能充分化解非系统风险,存在所谓的规模效应;训练后的网络预测显示,高阶CAPM无论是在预测精度还是预测稳定性上都要明显优于传统的CAPM,在一个非系统风险得到充分分散的证券组合中,加入三阶矩的CAPM已经能够比较准确地把握风险资产的市场定价。     

基于小波变换的形状识别方法

<正> 在我国的改革开放中,金融改革始终是国内外关注的一个焦点。自1996年12月1日起,我国政府开始接受国际货币基金组织(IMF)协定第八条的义务,实行人民币经常项目下的可兑换。在此以前不久,我国政府已经开始了允许外资银行经营人民币业务的试点;同时,正在考虑和即将开始允许外国投资基金进入我国资本市场的试点。这些都表明了我国金融市场正在扩大对外开放的程度,也表示了政府对金融自由化与国际化所持的积极态度。 在我国的金融自由化与国际化进程中,资本市场(这里主要指证券市场)的对外开放是一个十分复杂和风险较大的问题。总体上,这里涉及到三个方面的问题:一是我国资本市场对外开放所应选择的入口和顺序;二是我国资本市场开放的制度障碍;三是我国资本市场对外开放的风险控制。     

基于离散小波变换和奇异值分解的数字水印算法

当前数字作品的版权保护问题备受关注,数字水印是保护数字作品版权的有效手段。本研究针对大容量水印嵌入问题,提出一种基于离散小波变换和奇异值分解的灰度图像数字水印算法。首先,利用离散小波变换将原始图像分为LL、LH、HL和HH四个子带;其次,通过奇异值分解获得LL子带图像的对角矩阵S^l以及水印图像的对角矩阵S^w;然后,将矩阵S^l与S^u进行叠加来完成水印嵌入。该算法的突出特点为嵌入的水印容量大,且算法具有较强的鲁棒性。实验证明,嵌入水印后的图像在经受高斯噪声、椒盐噪声、旋转等攻击后,利用该算法仍能提取到有效的水印图像。     

Oil price and automobile stock return co-movement: A wavelet coherence analysis

This paper explores possible co-movement between oil price and automobile stock return in a joint time-frequency domain. Daily price series from August 01, 1996 to June 20, 2017 is used in this analysis. The results indicate that the co-movement between oil price and automobile stock return is strong during November, 2000–December, 2002 and March, 2006–December, 2009. The co-movement is found to be more pronounced in the long-term and stock return is sensitive to the higher oil price emanating from the demand shock. This contravenes the conventional wisdom that crude oil is always counter-cyclical to the automobile stocks. For investor, this weakens the probable gain from including oil asset in a portfolio of automobile stocks as crude oil does not offer cushion against bearish automobile stock markets during the crisis period.     

Revisiting global economic activity and crude oil prices: A wavelet analysis

Based on the wavelet analysis approach, this paper firstly examines the dynamic relationship between global economic activity (proxied by the Kilian economic index) and crude oil prices in both time- and frequency-domains. Our empirical results demonstrate significant correlation between crude oil prices and global economic activity at high frequencies (in the short run) during the entire sample period; however, the co-movement between the two at low frequencies (in the long run) is weaker and exists only during certain proportions of the sample period. We also document evidence that global economic activity and oil price are positively correlated, with dynamic lead-lag relationships across time. Our findings are robust to alternative choices of oil price indexes and controlling for other confounding factors such as geopolitical risk, armed conflicts, economic policy uncertainty and equity market uncertainty. The current study provides valuable implications for oil market investors based on the information of global economic situation and its dynamic relationships with oil prices.