Volatility Transmission in Emerging European Foreign Exchange Markets

By Vit Bubak, Evézen Kocenda, Filip Žikeš. Social Science Research Network, 2011

338 citations recorded by the source corpus.

This paper studies the dynamics of volatility transmission between Central European currencies and euro/dollar foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the Central European foreign exchange markets. With the exception of the Czech currency, we find no significant spillovers running from euro/dollar to the Central European foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold-Yilmaz volatility spillover index, and show that volatility spillovers tend to increase in periods characterized by market uncertainty.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/eba9ce61b45036366cb5e7e7427de8bafe76350e

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Citations in the corpus

On the Linkages between Stock Prices and Exchange Rates: Evidence from the Banking Crisis of 2007-2010 (2014, result): “Aloui et al. (2011), Kenourgios et al. (2011), Samarakoon (2011), and Dufrénot et al. (2011) among others find indeed an increase in dependence between international stock markets, and similar findings are reported by Coudert et al. (2011) and Bubak et al. (2011) for foreign exchange markets.”
How strong is the linkage between tourism and economic growth in Europe (2015, background): “…spillover index has already attracted a considerable attention in the economic literature (see, inter alia, McMillan and Speight, 2010; Yilmaz, 2010; Bubák et al., 2011; Antonakakis, 2012; Zhou et al., 2012; Antonakakis and Badinger, 2014) and this is its first application to the tourism context.”
Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions (2020, background): “…the stock market interdependencies, volatility spillovers, business cycle spillovers, as well as bond yield spillovers (see, inter alia, Alter and Beyer 2014; Antonakakis 2012; Awartani and Maghyereh 2013; Bekaert et al. 2014; Bubák et al. 2011; Diebold and Yilmaz 2015; McMillan and Speight 2010).”
Refined Measures of Dynamic Connectedness based on TVP-VAR (2017, background): “…volatility spillovers, business cycle spillovers and bond yields spillovers (see, inter alia, McMillan and Speight, 2010; Yilmaz, 2010; Bubák et al., 2011; Antonakakis, 2012; Zhou et al., 2012; Antonakakis and Vergos, 2013; Antonakakis and Badinger, 2014; Narayan et al., 2014;…”
Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers (2015, neutral): “2The DY index has been rapidly adopted in the relevant literature, for example, by McMillan and Speight (2010); Yilmaz (2010); Bubák et al. (2011); Fujiwara and Takahashi (2012); Kumar (2013); Fengler and Gisler (2015).”
Oil shocks and stock markets: dynamic connectedness under the prism of recent geopolitical and economic unrest (2017, background): “We highlight that the issue of interdependence (either in a static or a time–varying environment) between financial markets and its importance for portfolio managers, asset pricing model and forecasting has been extensively studied in the literature (see, for instance Apergis et al., 2016; Baruńık ”