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Market links (and price discovery) between financial assets and lead–lag relationships are topics of interest for financial economists, financial managers and analysts. The lead–lag relationship analysis should consider both short and long-term investors. From a portfolio diversification perspective, the first type of investor is generally more interested in determining the co-movement of financial assets at higher frequencies, which are short-run fluctuations, while the latter concentrates on the relationship at lower frequencies, or long-run fluctuations. The paper aims to discuss these issues.

For this study, a technique was employed known as the wavelet approach, which has recently been imported to finance from engineering sciences to study the co-movement dynamics between global sukuk and bond markets. Data cover the period from January 2010 to December 2015.

The results indicate that: there is no unidirectional causality from developed market bond indices to Malaysia and Dow Jones indices, which is promising for fixed-income investors of a developed market; and in relation to emerging markets, the Malaysian sukuk market has a bidirectional causality with Indonesia, Malaysia, India and South Korea bond indices but not China bond indices, while in terms of the Dow Jones sukuk index, there is no unidirectional causality between the listed emerging markets and the sukuk index except Indonesia’s market during the sample period.

This analysis provides evidence regarding the timely and appropriate measure of correlation changes and the behaviour of sukuk and bond indices globally, which is beneficial to the management of sukuk and bond portfolios.

The evidence hitherto unexplored, which was produced by the application of a wavelet cross-correlation amongst the selected sukuk and bond indices, provides robust and useful information for international financial analysts as well as long and short-term investors.

The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.

The analysis uses a novel way to study contagion using wavelet methods. The comparison is made between co‐movements at different time scales. Co‐movement methods of the discrete wavelet transform and the continuous wavelet transform are applied.

Clear signs of contagion among the major markets are found. Short time scale co‐movements increase during the major crisis while long time scale co‐movements remain approximately at the same level. In addition, gradually increasing interdependence between markets is found.

Because of the chosen method, the approach is limited to large data sets.

The research has practical implications to portfolio managers etc. who wish to have better view of the dynamics of the international equity markets.

The research uses novel wavelet methods to analyze world equity markets. These methods allow the markets to be analyzed in the whole state space.

The purpose of this paper is to derive crucial insights from multi-scale analysis to detect equity return co-movements between Korean and emerging Asian markets.

Wavelet correlation, wavelet coherence and wavelet clustering measures are used to uncover Korean equity market interactions which are hard to see using any other modern econometric method and which would otherwise had remained hidden.

The authors observed that Korean equity market is strongly integrated with Asian equity markets at lower frequency scales and has a relatively weak correlation at higher frequencies. Further this correlation eventually grows strong in the interim of crises period at lower frequency scales. The authors, however, do not found any significant deviation in dendrograms generated in data clustering process from wavelet scale 2 to 6 which are associated with four and 64 weeks period, respectively. Overall the findings are relevant and have strong policy and practical implications.

The unique contribution of this paper is that it introduces wavelet clustering analysis to produce a nested hierarchy of similar markets at each frequency level for the first time in finance literature

This study aims to investigate the dynamic co-movement and interconnection among 69 security investment indices in China using the multi-time scale framework.

The authors first use the multiple coherence analysis method to exhibit the degree of relationships among the variables under study. In addition, the wavelet multiple correlation and wavelet multiple cross-correlation analyses are used to examine the time-frequency synchronization interdependence structure among the variables.

From the empirical findings, one may infer less opportunity for portfolio diversification at higher time scales. Obviously, at these scales, the authors find that the 69 Chinese investment indices generate a simple security investment class, as indicated by higher interconnection between the indices.

Further research can increase the sample size to re-investigate the empirical relationship for security investment indices.

In the nutshell, the results demonstrate the potential for Chinese investors to invest in security investment indices to earn from portfolio diversification at lower time frequencies. The Chinese investment market indices under study yield further opportunities of portfolio diversification toward the short-term investors than the long-term investors.

To the best of the authors’ knowledge, this is the first paper to examine the dynamic co-movement and interconnection for security investment indices in China.

This research paper aims to discuss the effects of exchange rates on interest rates by using wavelet network methodology, which is a combination of wavelets and neural networks.

The paper employs wavelet networks to analyse the relationships between the financial time series. Empirically, the research examines the effects of foreign exchanges on the interest rates in Turkish financial markets by using daily USD/TRY rates and interest rates in Turkish Lira (TRY).

The results indicate that the wavelet network model is the most successful methodology among the alternatives such as Hodrick‐Prescott filter, feed‐forward neural network, wavelet causality, and wavelet correlation analysis in capturing the non‐linear dynamics between the selected time series.

The research results have both methodological and practical originality. On the theoretical side, the wavelet network is superior in modelling the causal linkages of the financial time series. For practical aims, on the other hand, the results show that the level of the effects of the exchange rates on the interest rates varies on the time‐scale used. Wavelet networks shows that the causality relationship is strong in the short run, while the effect decreases in the mid‐run.

A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.

The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.

Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.

The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.

This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

This paper aims to study the co-movement dynamics of Islamic equity returns to explain international portfolio diversification opportunities for investors having a heterogeneous stock holding period in light of Brexit.

The authors use the following three recent methodologies: the multivariate generalised autoregressive conditional heteroskedastic-dynamic conditional correlations, continuous wavelet transforms and maximum overlap discrete wavelet transform. Dow Jones Islamic country-based indexes are used from 2 September 2013 to 31 December 2019.

There is a high correlation between the United Kingdom (UK) Islamic stock market return with the Canadian, USA, Malaysian and Indian implying lesser diversification benefits for the investors. However, the results tend to indicate that UK Islamic stock market investors who have allocated their investment in Sri Lanka, Kuwait, Japan and Turkey have enjoyed diversification benefits. Besides, there is a declining correlation between UK Islamic stock markets and other selected markets aftermath of Brexit. Turkey seems the most volatile stock over the period, appealing to risk-lover investors to gain from price changes. When the shock occurs in the financial sector, the volatility is mean-reverting faster than other markets in Sri Lanka. On the other hand, Malaysia appears to have the least volatility implying a stable financial sector.

The results tend to shed light on effective portfolio diversification benefits in light of the recent shock (Brexit) between the UK Islamic stock index and other selected indexes that vary from country to country depending on investment horizons. This critically confirms the significance of heterogeneity in investment horizons and provides significant inferences for portfolio diversification strategies.

To the best of the authors’ knowledge, this study is the first study investigating the Brexit effect on Islamic stocks, guiding Shariah sensitive investors in their diversification strategies, providing information to investors to consider the implications of this incident on Islamic stocks for future shocks.

The rising interconnectedness between international banks, at one end, allow participants to share risk and diversification which leads to stable local lending and increase in competitiveness, however, at the other end poses potential for volatility spillover and thereby contagion phenomena. Therefore, investigating the presence of co-integration amongst international banks can provide useful information about risk spillover in times of financial turbulence

The authors employ wavelet correlation and wavelet multiple cross-correlation strategies, following an initial decomposition of returns series through maximal overlap discrete wavelet transformation (MODWT).

The results indicate high integration level between Citibank and Deutsche Bank whereas potential of diversification exists between pairs of Citibank–Hong Kong and Shanghai Banking Corporation and Bank of America–Deutsche Bank, with former more evident in short- and medium-term relationship and later in long-run investment horizon. This paper carries implications for investors, fund managers and policymakers in foreseeing the prospects of contagion attributable to high level integration levels.

Implications for cross-border banking integration includes the presence of common lender effect which appears as a dominant factor for cross-border contagion. Therefore, banks based in different countries should focus more on funds diversification rather than borrowing much from any single creditor. Furthermore, foreign operations based on subsidiaries instead of relying on direct cross-border lending can help in reducing volatility of the foreign financial resources. Nevertheless, based on the results and significant strand of existing literature, the presence of contagion is inevitable, and therefore, a careful consideration of cross-border banking supervision and co-operation by the financial authorities can help in mitigating the volatility of global capital flows.

First, this study fills gap in the existing literature regarding the discussion on portfolio diversification opportunities in the banking sector. The banking sector is usually perceived as a main source of fixed income securities or financing; however, on the contrary, investors may also be interested for investments in publicly listed bank's stock. Most of the work regarding portfolio diversification revolves around capital market instruments; however, publicly listed shares of largest bank also present an avenue for diversification. Second, major fundamentals and the associated factor for banks performance are reflected in the its profits, either these profits result from large customer base or proper allocation of bank's assets, etc. Therefore, returns of these banks serve as a barometer for their performance and co-movement between any two banks can highlight the presence and extent of their underlying association. Third, the authors apply the latest extensions in wavelet techniques after decomposing returns series through the MODWT framework. This decomposition followed by wavelet estimations allow us to investigate banks integration level across different time and frequency space thereby carrying implications for both short- and long-run investors. Fourth, by analysing the presence of returns co-movements, the authors can predict the extent of plausible contagion since the recent global financial crisis of 2008–2009 used banks as the main medium of propagation of shocks. Fifth, the work presents many implications for the investment community, major trading partners associated with banks through different instruments and for policymakers so that the effect of contagion can be anticipated or at least mitigated in case of future financial turbulence.

Highlights

1. We investigate portfolio diversification opportunities in the banking sector.

2. Time-frequency returns co-movements is measures by applying wavelet multiple correlation and cross-correlation techniques on decomposed return series.

3. Deutsche Bank and Bank of America act as highest transmitter and recipient of volatility, respectively using the spillover approach of Diebold and Yilmaz (2012).

4. Citibank and Deutsche Bank exhibit high pairwise correlation indicating no diversification benefits.

5. Citibank exhibits high level of integration with other banks in the short- and medium-run whereas Deutsche Bank exercises high integration levels in the long-run investment period.

We investigate portfolio diversification opportunities in the banking sector.

Time-frequency returns co-movements is measures by applying wavelet multiple correlation and cross-correlation techniques on decomposed return series.

Deutsche Bank and Bank of America act as highest transmitter and recipient of volatility, respectively using the spillover approach of Diebold and Yilmaz (2012).

Citibank and Deutsche Bank exhibit high pairwise correlation indicating no diversification benefits.

Citibank exhibits high level of integration with other banks in the short- and medium-run whereas Deutsche Bank exercises high integration levels in the long-run investment period.

The purpose of this paper is to study the co-movement and market integration dynamics of the emerging/frontier stock markets in South Asia (India, Pakistan and Sri Lanka) with a portfolio management perspective.

Scholars in the past have documented the limitation of standard econometric techniques such as co-integration analysis to capture this phenomenon. The other econometric technique widely used in integration and comovement literature is dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity. This method captivates the time-varying correlations, although frequency information is absent. The wavelet-based analysis decomposes the time-series data in a time-frequency domain, which is largely useful to fund managers and policy makers. This study examines the regional integration in selected South Asian markets using wavelet analysis.

The results suggest some degree of market integration, however weak as compared to regional integrations in developed markets. Pakistan and India were found to be the potential leaders at varying time scales in the region. Weaker co-movement phenomena may offer ample arbitrage opportunities to investors in this region. In addition, the authors also find that the structure of correlation changes after some of the major macroeconomic events.

This study is among the first to examine co-movement and integration of stock returns in a time-frequency domain for South Asia. In addition, the authors also highlight weak integration in these markets, which may be beneficial for portfolio diversification.

The 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.

This 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.

The 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.

This 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.