Revisiting Covered Interest Parity in the European Union: the DCCA Approach

Ferreira, Paulo; Dionísio, Andreia

doi:10.1080/10168737.2015.1081260

Cited by 24 publications

(4 citation statements)

References 21 publications

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“…In the long run, countries like Romania, Bulgaria, and Croatia had little or no evidence of significant correlations. This was in line with the results of other work (see, for example, Ferreira and Dionísio 2015;Ferreira et al 2016). It is worth noting that after Brexit, there was a tendency towards a decrease in comovements for the majority of countries.…”

Section: Evolution Of the Dcca Correlation Coefficients Over Timesupporting

confidence: 92%

EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients

Tilfani

Ferreira

Dionísio

et al. 2020

JRFM

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For this paper, we dynamically analysed the comovements between three major stock markets—Germany, the UK, and the US—and the countries of the European Union, divided into two groups: Eurozone and non-Eurozone. Correlation coefficients based on a detrended cross-correlation analysis (DCCA) were used, and the respective temporal variation was evaluated. Given the objective of performing a dynamic analysis, sliding windows were used in an attempt to represent short and long-term analyses. Critical moments in financial markets worldwide were also taken into account, namely the subprime debt crisis, the sovereign debt crisis, and Brexit. The results suggest that Germany and other Eurozone countries generally share high levels of comovements, although the Brexit decision reduced those connections. The subprime crisis also increases comovements among markets.

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Section: Evolution Of the Dcca Correlation Coefficients Over Timesupporting

confidence: 92%

EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients

Tilfani

Ferreira

Dionísio

et al. 2020

JRFM

Self Cite

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“…More specifically, we evaluated the co-movements during the pre-crisis period (up to 31 December 2019) and during the crisis period (from this cut-off date until 30 January 2021), thus allowing us to make conclusions regarding integration, contagion, or independence, in accordance with the adopted definition of contagion (see [26]), as well as the studies of [41] or [80]. The non-linearity of data makes the use of classic linear approaches inappropriate; thus, the evaluation of a contagion between cryptocurrencies is based on the DCCA (commonly used in the finance literature, see for example [81][82][83][84]), the ρDCCA, and variations thereof. DCCA does not require that the analyzed series are stationary, and it allows the establishment of cross-correlations (contagion effects) in both regimes by directly using the properties of the moments of the series (either linear or nonlinear relationships).…”

Section: Methodsmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis

Almeida

Dionísio

Ferreira

et al. 2023

FinTech

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Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrence—the COVID-19 pandemic—on cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of ‘correlations breakdown’ in times of crisis.

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“…This approach has applications in finance, [53][54][55][56][57][58][59], climatology [60] and criminology [61]. An advantage of this approach compared to other correlation coefficients, such as Pearson correlation coefficient, is that it is a multiscale correlation method and it is possible to obtain correlations for several timescales, both linear and nonlinear [62].…”

Section: Plos Onementioning

confidence: 99%

Conservation in the Amazon rainforest and Google searches: A DCCA approach

Pereira

Ferreira²,

Lima³

et al. 2022

PLoS ONE

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In this paper we analyze the descriptive statistics of the Google search volume for the terms related to the National Reserve of Copper and Associates (RENCA), a Brazilian mineral reserve in the Amazon of 4.6 million hectares, before and after the government signed the decree releasing it for exploration. First, we analyze the volume of searches for expressions related to RENCA in Google Trends using descriptive statistics; second, we assess the cross-correlation coefficient ρDCCA, which measures the cross-correlation between two nonstationary time series across different time scales. After the government announced the release of the RENCA reserve, there was an increase in the average volume of Google searches for related terms, showing people’s concern about the announcement. By using the cross-correlation coefficient ρDCCA, we identify strong cross-correlations between the different expressions related to RENCA in Google Trends. Our work shows the utility of Google Trends as an indicator of the perception of environmental policies. Additionally, we show that ρDCCA can be used as a tool to measure the cross-correlation between synonyms extracted from Google Trends for various time scales.

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Revisiting Covered Interest Parity in the European Union: the DCCA Approach

Cited by 24 publications

References 21 publications

EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients

EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients

Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis

Conservation in the Amazon rainforest and Google searches: A DCCA approach

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