Growth cycle synchronization of the Visegrad Four and the European Union

Hanus, Lubos; Vácha, Lukáš

doi:10.1007/s00181-018-1601-x

Cited by 12 publications

(12 citation statements)

References 37 publications

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“…Next, we describe the wavelet methods used to measure the bivariate comovements between currency realized variances and semivariances. Finally, we explain the measurement of dynamic relationships among the realized volatilities of currencies using a wavelet cohesion measure with time‐varying weights recently proposed by Hanus and Vácha (2020). The realized semivariances enable us to account for asymmetries (unexpected upside and downside changes) in the distribution of currencies' volatilities.…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…A phase difference equal to zero indicates that a pair of currencies comove at a certain frequency. Following Torrence and Compo (1998) and Hanus and Vácha (2020), we evaluate the significance of both measures by utilizing Monte Carlo simulation method and assess the significance portrayed in figures as a black contour and at the 5% significance level.…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…The wavelet coherence primarily provides an overview of time‐frequency connectedness in a bivariate scenario. We follow Croux, Forni, and Reichlin (2001), Hanus and Vácha (2020), and Rua and Lopes (2015) to evaluate the connectedness among multiple currency volatilities over the frequencies, termed as cohesion. Let x t = ( x 1 t , …, x nt ), n ≥ 2 be the multiple time series, then cohesion among the series in the frequency domain is defined as:

italiccoh ((), λ) = \frac{\sum_{i \neq j} w_{i} w_{j} ρ_{x_{i} x_{j}} ((), λ)}{\sum_{i \neq j} w_{i} w_{j}}, italiccoh ((), λ) \in ([], - 1, 1),

where λ represent the frequency, − π ≤ λ ≤ π , w i corresponds a weight associated with series x it .…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…This can be defined as:

ρ_{x_{i} x_{j}} ((),, τ, s) = \frac{frakturR ((), W_{x_{i} x_{j}} ((),, τ, s))}{\sqrt{{(||, W_{x_{i}} ((),, τ, s))}^{2} {(||, W_{x_{j}} ((),, τ, s))}^{2}}}, ρ_{x_{i}, x_{j}} ((),, τ, s) \in ([], - 1, 1),

where

frakturR (() \cdot)

represents the real part of the wavelet cross‐spectrum, called the co‐spectrum of two series. Following Hanus and Vácha (2020), we capture the underlying dynamics and development of currencies by mapping the multivariate relationship utilizing the time‐varying weights in the wavelet cohesion measure as:

italicco h^{TV} ((),, τ, s) = \frac{\sum_{i \neq j} w_{ij} ((), τ) ρ_{x_{i} x_{j}} ((),, τ, s)}{\sum_{i \neq j} {\overset{false¯}{w}}_{ij} ((), τ)}, italicco h^{TV} ((),, τ, s) \in ([], - 1, 1),

where w ij ( τ ) represents the weights attached to the pair of time series ( i , j ) at given time τ . The wavelet cohesion with dynamic weights enable us to use various types of weights, for instance, gross domestic product (GDP) and gross national product (GNP), among others.…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…4 This methodological approach, considering volatility jumps and the incorporation of positive and negative semi variances in the wavelet coherence model, adequately characterizes the co‐influence between currencies' volatilities (Bekiros, 2014; Fernández‐Macho, 2018; Rua & Nunes, 2009). 5 Following Hanus and Vácha (2020), we also utilize the wavelet cohesion with dynamic weights to capture the commonalities among the underlying currencies.…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric interdependence between currency markets' volatilities across frequencies and time scales

et al. 2020

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We investigate the dynamics of interdependence between realized variances and realized semivariances of six major currencies across frequencies and time scales. The empirical results are derived, first, through constructing daily measures of realized variance and semivariance from a high frequency 5‐min interval data, and second, by fitting wavelet squared coherence and wavelet cohesion measure with time‐varying weights. The realized volatilities of the currencies and their cross‐currency influences are found to increase during the global financial crisis. The realized volatilities of the Euro, Swiss Franc, and British Pound are closely synchronized over the short‐term horizon. However, over the long‐run, the Euro, Swiss Franc, and Japanese Yen lead the realized volatilities of the British Pound, Australian Dollar, and Canadian Dollar. The synchronization structure of positive and negative realized volatilities indicates asymmetric dependence among the currencies across time horizons. We further observe strong positive (negative) cohesion among the realized volatilities over the medium‐ and long‐term horizons. Finally, significant counter cyclical comovements among the currencies are observed over the medium‐ and long‐term horizons. These findings have important implications for foreign exchange portfolio managers.

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Section: Econometric Frameworkmentioning

confidence: 99%

Section: Econometric Frameworkmentioning

confidence: 99%