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This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

The purpose of this paper is to discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is to examine the relationship between stock returns and Fama‐French risk factors at different time‐scales.

Exploiting the scale separation property inherent to the maximal overlap discrete wavelet transform, the data set are decomposed into components associated with different time‐scales. This wavelet‐based decomposition scheme allows the three Fama‐French models to be tested over different investments periods.

The obtained results show that the explanatory power of the Fama‐French three‐factor model becomes stronger as the wavelet scale increases. Besides, the relationship between the portfolio returns and the risk factors (i.e. the market, size and value factors) depends significantly upon the considered time‐horizon.

The proposed methodology offers investors the opportunity to construct dynamic portfolio management strategies by taking into account the multiscale nature of risk and return. Moreover, it gives a new insight to fund rating and fund selection issues in relation to heterogeneous investments periods.

The paper uses wavelets as a relatively new and powerful tool for statistical analysis that allows a new understanding of pricing models. The paper will be of interest not only for academics in the field of asset pricing but also for fund managers and financial market investors.

This study aims to examine the spillover effects of the mean and volatility between oil prices and stock indices of six Gulf Cooperation Council (GCC) countries (UAE, Kuwait, Saudi Arabia, Qatar, Oman and Bahrain).

Over the period 2008–2019, a bivariate VARMA-GARCH-ADCC model was combined with the maximal overlap discrete wavelet transform technique filter to shed light on a wide range of possible spillover effects in the mean and variances of level prices at various time horizons.

The authors find that the spillover effects between oil prices and the GCC stock markets are time-varying and spread across various time horizons. Besides, oil prices and stock market indices are directly impacted by their own shocks and variations and indirectly influenced by other price volatilities and wavelet scales. The linkages in volatility spillovers between oil prices and the GCC stock markets occur in the short-term, midterm and long-term horizons. More specifically, the results also show that the asymmetric estimates are statistically significant for the associations between oil prices and each stock market in the GCC countries. This implies that negative shocks play a more vital role than positive shocks in driving the dynamic condition correlations between oil and stock markets under study.

The significant interrelatedness between oil prices and each stock market in the GCC countries has important implications for investors, portfolio managers, and other market participants. They can use the findings of this research to create the best oil-GCC stock portfolios and predict more precisely the volatility spillover patterns in constructing their hedging strategies.

In several ways, this study differs from previous research. First, while previous empirical studies of the dynamic link between oil prices and stock markets have focused primarily on developed or emerging markets, the focus of this is on six GCC countries. Second, the linkage between oil prices and stock markets is typically studied at the original data level in the time domain in relevant literature, while frequency information is overlooked. Therefore, the current study examines this relationship from a multiscale perspective. Third, in this paper, to capture a wide range of possible spillover effects in the mean and variance of level prices at multiple wavelet scales, the authors use a VARMA-GARCH-ADCC model in conjunction with wavelet multiresolution analysis. Additionally, this article also applies wavelet hedge ratio and wavelet hedge portfolio analysis at various time horizons.

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.

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.

On the backdrop of growing importance of Shariah compliant equity markets, the purpose of this paper is to study cross-country portfolio diversification benefits for investors with major trading partners of Saudi Arabia, namely, USA, China, Japan, Germany and India, who have already invested or tend to invest in Saudi Arabian stock market.

The authors have investigated time invariant, dynamic correlations at different investments horizons of the investors among Islamic asset classes by applying relevant econometric techniques like multivariate generalized autoregressive conditional heteroscedastic –DCC and continuous wavelet transforms. For robustness, this study also applied maximal overlap discrete wavelet transform.

The findings tend to indicate that the Saudi Arabian investors have portfolio diversification benefits with all major trading partners in the short-term investment horizon. Interestingly, Saudi Arabian market has the least portfolio diversification benefits with the Chinese market. However, in the long run, all markets are correlated, yielding minimum portfolio diversification benefits and most importantly Saudi Arabian investors have portfolio diversification benefits with the Indian Islamic equity market in almost all investment horizons. The findings are highly consistent across different econometric technique estimations.

The authors are only considering five major trading partners of Saudi Arabia. Also, the authors are using S&P and FTSE shari’ah index. Moreover, the time period of the study is constrained by the availability of shari’ah indices. Econometric limitations are also well documented in the literature.

The results could be beneficial for the investors, portfolio managers, hedge fund managers and institutional investors and also could be useful for the policy makers in their policy-making decisions.

Only very few studies have looked into the benefits of international portfolio diversification from the perspective of local investors as well as the portfolio diversification benefits with the major trading partners of Saudi Arabia. One of the novelties of the method is to make the stock investors, practitioners and policy makers aware of the portfolio diversification benefits available at different time scales such as 4, 8, 16, 32, 64 and 256 trading days as investment holding periods to unveil the true dynamics of co-movement between those different assets.

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.

This study aims to examine the impact of terrorism on return and systematic risk of Pakistan’s equity industries. Daily data from 1 January 2000 to 31 December 2014 for 12 industries based on the specific types of companies listed on Karachi Stock Exchange are used for the empirical analysis.

A multiplicative (additive) term is introduced in the standard capital asset pricing model to examine the change in systematic risk (industry returns) in response to the terrorist activities. The authors use the multiscale beta approach (Yamada, 2005) and the maximal overlap discrete wavelet transform (MODWT) to test the heterogeneous market hypothesis.

Terrorism activities increase the systematic risk for most of the industries and the negative impact on returns of banks and the financial industry. It is noted that terrorism positively impacts (increases) the industrial systematic risk mainly in short-run (between two and four days-time horizon).

The paper examines the impact of terrorism on a broad list of industries’ (banks, basic materials, chemicals, construction, consumer goods, consumer services, financials, industrials, minerals, oil and gas, textile and utilities) risk and return in Pakistan, using the multiscale beta approach (Yamada, 2005) and the MODWT methods.

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.