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Purpose 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. Design/methodology/approach 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”. Findings 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. Originality/value 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.

Shocks, jumps, booms and busts are typical large fluctuation markers which appear in crisis. Models and leading indicators vary according to crisis type in spite of the fact that there are a lot of different models and leading indicators in literature to determine structure of crisis. In this paper, we investigate structure of dynamic correlation of stock return, interest rate, exchange rate and trade balance differences in crisis periods in Turkey over the period between October 1990 and March 2015 by applying wavelet coherency methodologies to determine nature of crises. The time period includes the Turkeys currency and banking crises; US sub-prime mortgage crisis and the European sovereign debt crisis occurred in 1994, 2001, 2008 and 2009, respectively. Empirical results showed that stock return, interest rate, exchange rate and trade balance differences are significantly linked during the financial crises in Turkey. The cross wavelet power, the wavelet coherency, the multiple wavelet coherency and the quadruple wavelet coherency methodologies have been used to examine structure of dynamic correlation. Moreover, in consequence of quadruple and multiple wavelet coherence, strongly correlated large scales indicate linear behavior and, hence VARMA (vector autoregressive moving average) gives better fitting and forecasting performance. In addition, increasing the dimensions of the model for strongly correlated scales leads to more accurate results compared to scalar counterparts.

PurposeThis study aims to attempt to understand the joint co-movement of bank deposit rate and its main underlying determinants (foreign exchange rate (FX) rate, cross-currency swap rate and implied forward rate). The authors also compare time and frequency variant approaches in this dynamic.Design/methodology/approachThe authors examine bank deposit rates where multiple variables jointly interact, and the integration is time and frequency variant. The study applies both cointegration and wavelet coherence methods and conducts a comparative analysis. It investigates eight markets over 2005–2020 aiming to capture the impact of changing market conditions and degree of development.FindingsThe results are in line with cross-country interdependence, where we observe more robust evidence for co-movement during adverse economic conditions with higher correlation compared to other periods such as the 2007–2009 US mortgage crises, 2010–2012 Euro crises and 2019 pandemic. Moreover, wavelet analysis suggests deposit rate lags FX rate and leads cross-country swap rate. The USA arguably leads the co-movement accompanied briefly by Japan and followed closely by other developed markets and later the developing markets. Heat maps suggest clustering of countries.Practical implicationsThe wavelet coherence's ability to indicate the periods and the frequencies of the relationship is essential to capture the true nature of the relationship. Such additional insight would enable the practitioners to determine the true price of the deposit rate.Originality/valueThe study captures the long suggested collective nature of three main underlying determinants of bank deposit. The results shed light on the order of dynamics in a complex bank deposit environment. Comparative analysis further highlights the valuable insight quadruple wavelet coherence provides.

This paper investigates the multifractal behavior of the probability of default (PD) of real sector firms and Turkey sovereign credit default swap (CDS). Moreover, we emphasize the co-movements of Hölder exponents during the financial crisis periods. For this reason, first, it is necessary to figure out the default probabilities of real sector firms. The default probability is evaluated weekly by the methodology of Moody’s Analytics, which is a commonly used approach, in which the market value of a firm is a call option written on its total assets. Multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average cross-correlation analysis (MF-X-DMA) techniques are applied to identify the multifractal behavior of the large-scale fluctuations of PDs and CDSs. In this way, we can evaluate the local Hurst exponents. Besides, the oscillation method is employed to estimate the pointwise and local Hölder exponents. In the period between January 2001 and March 2018, the structure of dynamic co-movements of Hölder exponents is determined by applying wavelet coherency methodology and the relations in crisis period are revealed. The selected period covers the crises with structural differences: Turkey banking crisis, the US sub-prime mortgage crisis and the European sovereign debt crisis that occurred in 2001, 2008 and 2009, respectively. Besides, during the periods of financial crises, among the local Hölder exponents, severely correlated large scales show multifractal features, and hence vector fractionally autoregressive integrated moving average (VFARIMA) forecasting provides better results than scalar models.