Wavelet Multiple Cross-correlation Research Articles

Portfolio diversification benefits before and during the times of COVID-19: evidence from USA

This paper investigates the portfolio diversification benefits for Islamic and conventional investors in the USA with its major trading partners (United Kingdom, Canada, China, Japan, Malaysia, and Turkey) before and during the COVID-19 crisis period. Using daily data from 2007 to 2020, we employ three relevant time-varying and timescale-dependent techniques, the continuous wavelet transform (CWT) analysis, the wavelet multiple correlation (WMC), and the wavelet multiple cross-correlation (WMCC). The findings suggest that conventional and Islamic US investors who invest with major trading partners may reap large diversification benefits for very short investment horizons (4–8), except for Sharia Malaysia index. However, they may not reap benefits for investment horizons of 8–16 and longer, except for China. In addition, COVID-19 crisis caused a poor diversification opportunity for US investors regardless of the regime they follow (conventional or Islamic). Moreover, the American industrial market depicts a state of impending perfect market integration. Finally, the UK and Canada seem to be the potential market leaders in different wavelet scales. These findings yield important policy implications.

Comovement and spillover among energy markets: A Comparison across different crisis periods

We compare the comovement and spillover between returns of six developed energy markets during different crisis periods using wavelet multiple correlation and wavelet multiple cross-correlation. The considered countries include Canada, France, Italy, Japan, the UK, and the US for a period from July 5, 1999 to July 3, 2021. The results reveal strong correlations between the markets of France, Italy, the US, and the UK across different scales. More specifically, the energy markets of France and Italy show pronounced correlation across lower frequencies, while the energy market of Canada is a prominent net transmitter, with it having the most influence on the connectedness of all other markets. However, the Japanese and Canadian energy markets can provide diversification benefits when combined with other markets. These findings have significant implications for policymakers and investors in terms of how best to invest during various crisis periods.

An empirical analysis of the dynamic relationship between clean and dirty energy markets

This research provides an empirical analysis of the dynamic relationship between clean and dirty energy markets. Specifically, we use Brent crude, West-Texas-Intermediate (WTI) crude, OPEC oil, Crude oil Oman and Crude Oil Dubai to denote dirty energy markets and use the S&P Global Clean Energy Index and WilderHill New Energy Global Innovation Index as a representative of the clean energy market. The time-frequency wavelet's multiple cross-correlation and cross-quantilogram correlation are used as estimation techniques to examine time-dependent wavelet cross-correlation and directional predictability, respectively. We use daily returns spanning from November 2013 to September 2020. Findings from the cross-quantilogram correlation (CQC) results suggest heterogeneous quantile dependence dynamics from clean energy markets to dirty energy markets. Additionally, findings from the cross-quantile correlation results reveal positive and negative directional predictability between clean and dirty energy markets in high, medium and low quantile ranges. Second, results from the time-frequency wavelets multiple cross-correlation approach suggest that clean and dirty energy markets are marginally integrated at the lowest frequencies, with dirty energy emerging as a predictive power of clean energy. In addition, we also find that the co-movements between the clean and dirty energy sources are volatile in the medium and long term, thus reducing the medium- and long-term diversification sphere. These findings are relevant for portfolio managers and clean energy producers.

FINANCIAL SECTOR DEVELOPMENT AND REAL SECTOR GROWTH – ASSOCIATION, SPILLOVER AND CAUSALITY DURING PRE COVID AND COVID REGIMES

In this paper, we propose an alternative approach to understanding the relationship between financial sector development and real sector growth in India. We use stock market sectoral indices available on National Stock Exchange (NSE) like Capital Goods Index, FMCG Index, Energy Index, Infra Index, Metal Index, Realty Index, and Auto Index to represent the real sector. To represent the financial sector, we consider Bank Index and Financial Services Index separately. The proposed framework examines the relationships at a granular level to understand the extent of association, spillover, and causality. We also analyze the relationship between the financial sector and the real sector in Pre COVID and COVID periods separately. Our research methodology includes the use of Detrended Cross-Correlation Analysis (DCCA), Wavelet Multiple Correlation (WMC), Wavelet Multiple Cross Correlation (WMCC), Diebold-Yilmaz spillover Framework, and Non-Linear Causality Test. Our granular approach has enabled us to examine the relationships in different periods and we observe that the results change. The intensity of the relationships also is different during the COVID period as compared to Pre COVID period.

Interdependence of economic policy uncertainty and business cycles in selected emerging market economies

PurposeThe purpose of this paper is to investigate the interdependence between economic policy uncertainty (EPU) and business cycles within and among six emerging market economies (EMEs) from January 1999 to December 2018.Design/methodology/approachThis study adopts the wavelet multiple correlations and wavelet multiple cross-correlation (WMCC) based on the maximal overlap discrete transform estimator. This methodology simultaneously investigates how two or more time series variables move together continuously at both time and frequency domains.FindingsThe empirical results show that business cycles comove with EPU for both intra- and inter-country analysis, with the long term showing the greatest degree of interdependence. In intra-country comparisons, EPU has a positive correlation with consumer price index and a negative correlation with share price index. According to the WMCC results, EPU does not have any leading or lagging power within each EME, but rather import has both lead and lag power. The inter-country WMCC results are all significant, with Korea’s EPU leading/following all EMEs across all scales.Originality/valueThis study contributes to the ongoing debate about what causes business cycles to comove by investigating business cycle indicators (leader/follower) using a robust wavelet methodology. The authors propose new variables that can clearly reflect the outcome of economic policy actions and translate information about EPU shocks. The inclusion of the variables has altered the understanding of the relationship between EPU and business cycle fluctuations. Policymakers also gain new insights into the trends and patterns of EPU and business cycles, which will help them formulate and implement fiscal and monetary policies more effectively.

Wavelet decomposition approach for understanding time-varying relationship of financial sector variables: a study of the indian stock market

In this paper, we study the effect of overall stock market sentiment in India on sectoral indices and on individual stock prices in terms of co-movement, dependence and volatility transmission along with the magnitude and persistence of the effects. The study uses wavelet decomposition framework for breaking down different financial time series into time-varying components. Quantile Regression, Wavelet Multiple Correlation and Cross-Correlation analysis, and Diebold-Yilmaz spillover analysis are then applied to investigate the nature of dependence, association, and spillover dynamics. For further focus, we have considered different time periods separately to identify the effect of market phases. Interesting results are obtained with respect to persistence of shocks, both across and within time periods. These have implications with respect to understanding market behavior and also perception of sectors and stocks. Keywords: Wavelet Decomposition, SENSEX, Quantile Regression, Wavelet Multiple Correlation, Wavelet Multiple Cross Correlation, Diebold-Yilmaz Spillover.

Multivariate Co-movement Between Islamic Stock and Bond Markets Among the GCC: A Wavelet-Based View

In this study, we investigate the connectedness between sharia stock index and three Islamic bond yields within a global perspective of the Gulf Cooperation Council Islamic financial markets. The main novelty of the present study is that we extend previous studies by performing three wavelet variants in bivariate and multivariate frameworks, namely the wavelet multiple correlation, the wavelet multiple cross correlation and wavelet cohesion. The findings point out a significant changing pattern in the dynamic linkage between sharia stocks and Islamic bond yields in the time-frequency domain. A strong positive association is evidenced in the short horizons and a negative linkage is branded for longer time-scales. Some resemblances are found for the wavelet cohesion corroborating the existence of potential portfolios’ diversification opportunities at lower frequencies. The multivariate wavelet cross correlation unveils that the intensity of the co-movement reaches its zenith at high frequencies. These results are not similar to the bivariate wavelet coherence but are coincident with the wavelet cohesion approach, which may be due to the difference in dimensionality of the wavelet approaches. The implications of this study will be useful for Islamic portfolio managers, international investors and market regulators in better encircling the best ways to adopt a proactive knowledge of Islamic financial markets behavior.