Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods

Polanco-Martínez, Josué M.

doi:10.1007/s11071-019-04974-y

Cited by 23 publications

(6 citation statements)

References 83 publications

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“…Similar to Polanco-Martínez (2019), to present a dynamic relationship between two variables -the Hurst exponents of the Bitcoin and the MSCI Emerging Markets Index -the rolling window correlation coefficients are estimated. Specifically, the Spearman rank correlation with p-values is exploited, because this is more robust to non-linear relationships of the analysed data series.…”

Section: Methodsmentioning

confidence: 99%

Is Bitcoin an emerging market? A market efficiency perspective

Skwarek

2023

Central European Economic Journal

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Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.

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Section: Methodsmentioning

confidence: 99%

Is Bitcoin an emerging market? A market efficiency perspective

Skwarek

2023

Central European Economic Journal

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“…Furthermore, the MODWT can calculate the variance of the wavelets and their coefficients at different scales, and the variance estimator is asymptotically more efficient [ 43 , 44 ]. Finally, if the financial series are non-linear for different reasons, calculating a linear correlation or linear causality between them could result in non-significant results [ 45 ]. For this last reason, GC-TE provides a good fit due to the nonlinear relationship that could exist between the variables [ 31 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic

Coronado

Martinez

Estrada

et al. 2022

Entropy

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The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets’ volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America

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“…However, it is highly recommendable to try different window lengths, from short, medium, to long sizes (see e.g. [51][52][53] ). For instance, in the example presented in this paper, in addition to the window length of T/8 (168 years), we tried other four window lengths ( M = 42, 84, 337, and 675 years) and the WLMC heat maps for 84 and 337 years are quite similar (whereas the "extreme" M values of 42 and 675 years are not very different) to that of the corresponding one with a value of M = 168 years (results not shown, but can be obtained through the R code included in the Supplementary Information (Material) or upon request to the corresponding author).…”

Section: Definition and Estimation Of The Wavelet Local Multiple Corrmentioning

confidence: 99%

Dynamic wavelet correlation analysis for multivariate climate time series

Polanco-Martínez

Fernández-Macho

Medina‐Elizalde

2020

Sci Rep

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The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

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Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods

Cited by 23 publications

References 83 publications

Is Bitcoin an emerging market? A market efficiency perspective

Is Bitcoin an emerging market? A market efficiency perspective

Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic

Dynamic wavelet correlation analysis for multivariate climate time series

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