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The purpose of this paper is to present a coherence analysis to evaluate the resilience for a critical infrastructure (CI). This is the new way to evaluate the CI and demonstrate that the authors need to pass from the protection towards resilience.

The authors use two approaches for this research. First is a consequence-based approach to evaluate the resilience. This approach has been used many times for evaluating the interdependencies between CIs. The second is a systemic approach to characterize the system and doing the coherence analysis.

This paper presents a methodology to evaluate the coherence in a context of CIs protection. The coherence analysis in resilience is a new concept and the first result to the application seems very good for the user of the research.

The originality of this paper is the coherence analysis applied to a resilience evaluation. The criteria for coherence analysis is innovative and it is a new way to consider the resilience and the relation between an organization and it is partners. Another value is the need for a wider scope in the analysis of hazards and how to address them that includes the infrastructure system itself, but also other related organizations and infrastructure systems.

The purpose of this paper is to investigate the volatility spectral of five major public real estate markets, namely, the USA, the UK, Japan (JP), Hong Kong (HK), and Singapore (SG), during the pre- and post-global financial crisis (GFC) periods.

First, univariate spectral analysis is concerned with discovering price cycles for the respective real estate markets. Second, bivariate cross-spectral analysis seeks to uncover whether any two real estate price series share common cycles with regard to their relative magnitudes and lead-lag patterns of the cyclical variations. Finally, to test the contagion effects, the authors estimate the exact percentage change in co-spectral density (cyclical covariance) due to high frequencies (short run) after the GFC.

The authors find that whilst none of the public real estate markets examined are spared from the crisis, the three Asian markets were less severely affected by the GFC and were accompanied by a reversal in volatility increase three years post-global financial crisis. Additionally, the public real estate markets studied have become more cyclically linked in recent years. This is particularly true at longer frequencies. Finally, these increased cyclical co-movements measure the outcomes of contagion and indicate fairly strong contagious effects between the public real estate markets examined due to the crisis.

The implication of this research is that benefits to investors from international real estate diversification may not be as great during the present time compared to previous periods because national public real estate markets have become more correlated. Nevertheless, the findings do not imply the complete absence of diversification benefits. This is because although cyclical correlations increase in the short run, many of the correlation values are still between low and moderate range, indicating that some diversification benefits may still be realized.

Given the significant market share and the highest levels of securitization in Asia-Pacific markets including JP, HK/China, and SG, this cyclical research including major public real estate markets has practical implications for ongoing international real estate investment strategies, particularly for the USA/UK and Asian portfolio managers.

This paper contributes to the limited research on the cyclical return and co-movement dynamics among major public real estate markets during financial/economic crisis in international finance. Moreover, the frequency-domain analysis conducted in this paper adds to better understanding regarding the impact of GFC on the cyclical return volatility and co-movement dynamics of major developed public real estate markets in international investing.

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.

This paper aims to investigate the linear and nonlinear interactions between the blockchain technology index and the UAE stock market index within the context of the Abu Dhabi and Dubai banking sector.

In this study, linear analysis was performed using the generalized autoregressive conditional heteroscedasticity model (GARCH) (1,1) model, whereas nonlinear analysis was performed using the wavelet coherence model.

Based on the results of the GARCH (1) model, the authors find that the blockchain technology index has a positive significant impact on stock market returns in the Abu Dhabi and Dubai banking sector. In addition, the findings indicate that increasing blockchain integration in the banking industry decreases banks’ stock market volatility and facilitates price stabilization. Additionally, the coherence wavelet analysis reveals that there is a phase relationship between the blockchain technology index and banks’ stock market indices in the banking sector of the UAE. The association was stronger during the global pandemic crisis because they were moving together across different timescales.

With the help of the linear analysis, this study offers a focal point and valuable insights to policymakers, central banks and commercial banks management on how implementing blockchain technology in the banking industry help boost stock market returns, reduce volatility and facilitate price stability. As a result of the nonlinear analysis of the significant long-term degree of co-movement between blockchain technology and banks’ stock markets in UAE, policymakers or the management of banks in UAE should take the growth of the blockchain technology industry into consideration to ensure the continued development of the banking sector. For investors, the findings provide implications for portfolio managers operating in the UAE who are encouraged to take short-term co-movement into account (1–16-week horizons) through both frequency and time when designing their portfolio while keeping long-horizon periods in mind is not recommended.

It is a pioneering study that empirically examines the linear and nonlinear nexus between the blockchain technology index and banks’ stock market returns and price stability.

Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX).

The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed.

The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not.

BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.

Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework.

The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”.

Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin.

The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.

This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.

The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.

This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

This study aims to compare the safe-haven properties of different asset markets such as gold, dollar, oil and disaggregated real estate sector (house, plot and residential) against equity returns in Pakistan over the monthly period of January 2011–December 2020.

The authors use wavelet coherence to encapsulate the overall dependence and correlation of asset classes. Further, the authors also study the potential of diversification at the tail of returns distribution by applying the wavelet value-at-risk (VaR) framework.

The results of wavelet coherence show that the dependence is weaker (stronger) in the short (long)-term investment horizon. Moreover, the findings of wavelet VaR reveal that the degree of co-movement between gold and equity returns greatly affects the portfolio risk followed by residential property and oil.

The findings are beneficial for the individual investor, fund managers and financial advisors looking for the optimal portfolio combination that hedges the excessive negative movements in equity returns subject to the heterogeneity in the investment horizon.

This is a primary effort to estimate safe-haven investments opportunities at a large spectrum, including disaggregated real estate sector against stock returns in Pakistan. Moreover, this study uses wavelet coherence and wavelet VaR which have an advantage over traditional analysis for diversification.

This study aims to close a gap in the relevant literature by investigating the causal linkage between financial risk (FR) and economic risk (ER) in China for the period 1985Q1–2018Q4.

Based on the aim of the present study, Toda Yamamoto causality and wavelet coherence tests are used to capture the relationship between FR and ER in China.

The findings from wavelet coherence reveal that there is feedback causality between FR and ER in China at different frequencies and different periods between 1985 and 2018. The consistency of the findings from wavelet coherence is confirmed by the outcomes of Toda Yamamoto causality test.

Although this study provides strong and consistent empirical findings for China, further studies should consider advancing the argument by focusing on different emerging markets.

Results are crucial for policy decision-making and can be used by researchers and macro-economic policymakers to take an action, if necessary, by implementing more appropriate or alternative economic and financial decisions.

To the best of the author’s knowledge, this relationship in China has not been comprehensively explored by using newly developed econometrics techniques. Therefore, this study is likely to open a debate about the literature as the study concludes with a discussion on short- and long-run implications for policymakers in China.