The impact of Euro through time: Exchange rate dynamics under different regimes

Antonakakis, Nikolaos; Chatziantoniou, Ioannis; Gabauer, David

doi:10.1002/ijfe.1854

Cited by 15 publications

(5 citation statements)

References 113 publications

(119 reference statements)

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“…Furthermore, all models allow for different marginal distributions, for example, normal, Student’s t, generalized error distribution (GED), or their skewed versions [ 56 ]. When selecting the most appropriate GARCH specification for each series, we follow an adjusted version of the BIC proposed by Antonakakis et al [ 57 ]: , where L represents the maximum of the likelihood function and k denotes the number of insignificant parameters and the number of significant misspecification tests (including sign bias, the weighted Li-Mak test, the 1% value-at-risk (VaR) test, the 1% conditional VaR test, and the 1% duration based VaR test).…”

Section: Methodsmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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The linkages between the US and China, the world’s two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

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Section: Methodsmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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“…We departed from the connectedness approach of Diebold and Yilmaz (2014) and performed the relevant analysis in terms of the TVP-VAR model in line with Antonakakis et al (2020), Antonakakis et al (2021), Balcilar et al (2021) Bouri et al (2021), Chatziantoniou and Gabauer (2021). In this context, Gabauer (2021) defines the TVP-VAR model as presented below.…”

Section: Empirical Modelmentioning

confidence: 99%

Stock Market TVP-VAR Dynamic Connectedness and VIX Shocks Spillovers: Evidence from a Sectoral Analysis of the Fragile Five

Jurkowska,

Ozcelebi,

Fijorek

2024

IER

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This study investigates the cross-sectoral spillover effect and the contribution of the volatility index (VIX) in the transmission of shocks in the Fragile Five (Brazil, India, Indonesia, South Africa, and Turkey). Research Design & Methods:We focused on the role played by the various sectors of the economy (Energy, Financials, Industrials, Basic Materials, and Real Estate) and the VIX in the crisis propagation, i.e. whether they act as sources, transmitters or receivers of financial stress, following the time-varying parameter vector autoregression (TVP-VAR) approach. The study also highlights the dynamic evolution of contagion across time. Findings: We identified the financial sector (essentially the largest in terms of market value) as a strong net transmitter. Another relatively strong net transmitter was the industrial sector (medium-sized). Basic Materials (medium-sized) and Real Estate (the smallest in terms of market value) essentially acted as net receivers. In all the countries surveyed, Energy (medium-sized sector) and the VIX were identified as net receivers. We found that the bidirectional spillover between the VIX and sectoral indices was weak in all countries under investigation. Our observations on the VIX behaviour can be associated with the fundamentals, such as the US/the Fragile Five equity market mutual exposures, but we opted for the neutrality of the relationship between the VIX and sectoral indices. The average Total Connectedness Index (TCI) in the Fragile Five turned out to be relatively low. We also observed that sharp increases in the TCI were associated with market tensions, both country-specific and global. Implications & Recommendations: Our conclusions can help formulate economic policy goals and increase the efficiency of portfolios with exposures in the Fragile Five countries. Risk transmitters such as the financial and industrial sectors should be closely watched by regulators and investors with exposures to risk takers, i.e. sectors such as Energy, Basic Materials, and Real Estate. Sharp increases in the TCI related to market tensions should be a warning signal when formulating economic policy goals and building investment strategies. Determining the role of fear indices in the spillover mechanism, including the VIX, requires additional in-depth empirical studies. Contribution & Value Added: Our contribution to the literature is threefold. Primarily, to our knowledge, it is the first study to provide insights into the linkages between the different sectors of the Fragile Five economies and the volatility index. Secondly, unlike the vast majority of previous work focused on sectoral linkages in selected groups of emerging economies taken as a whole, we draw common conclusions for the Fragile Five based on observations of their individual economies, which helped to reduce the number of generalizations. Thirdly, through the inference process, we identified some interesting correlations that could be a starting point for further research.

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“…The results further showed that the connectedness between currencies was much stronger, especially in cases of negative and positive shocks. Antonakakis et al (2021), examined the volatility of 6 currencies i.e. Australian dollar (AUD), British pound (GBP), Canadian dollar (CAD), Japanese yen (JPY), Swiss franc (CHF) and Euro (EUR) against the USD.…”

Section: Literature Reviewmentioning

confidence: 99%

TVP-VAR Frequency Connectedness Between the Foreign Exchange Rates of Non-Euro Area Member Countries

Akbulut,

Ari

2023

Folia Oeconomica Stetinensia

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Research background The main purpose of monetary integration between EU countries is to eliminate excessive fluctuations in exchange rates. High volatility in exchange rates can cause various negative economic and financial effects, especially during periods of economic shocks. In addition, estimating the volatility between currencies and their interactions is of great importance for effective portfolio management. Purpose The objective of this research is to scrutinize the transmission of volatility between the currencies of those European Union nations that do not participate in the EURO area, focusing on the exchange rate parity of the US Dollar with seven non-EURO zone currencies. Research methodology Daily volatility in exchange rates was calculated using the Garman-Klass-Yang-Zhang (GK-YZ) method. To investigate the connectedness between these volatilities, we used the Time-Varying Parameter Vector Autoregression (TVP-VAR) frequency connectedness approach. Results The Average Total Connectedness Index exhibits a significant degree of connectedness of approximately 71.84%. The Net Total Directional Connectedness Index indicates that the CZK, DKK and RON exchange rates are net beneficiaries in aggregate and in a longer term perspective, whereas the DKK, HUF and PLN exchange rates are net beneficiaries in a shorter term horizon. In the context of major global events such as the onset of the COVID-19 outbreak in March 2020 and the start of the Russia-Ukraine conflict in February 2022, it could be observed that the dynamic Total Connectedness Index exhibited a substantial increase, both overall and from a long-term perspective, corroborating theoretical expectations. According to the Net Pairwise Directional Connectedness index, the highest bilateral connectedness overall and in the short run was between DKK and RON, while in the long run between BGN and DKK. Novelty Examining the connectedness of currencies is of great importance for investors doing business with foreign currency, international cooperation and policies, risk management and portfolio management. Determining the connectedness in different frequency (short and long-term) ranges provides important information for hedging risk. In addition, the bilateral connectedness between currencies is a guide for effective portfolio diversification.

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The impact of Euro through time: Exchange rate dynamics under different regimes

Cited by 15 publications

References 113 publications

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Stock Market TVP-VAR Dynamic Connectedness and VIX Shocks Spillovers: Evidence from a Sectoral Analysis of the Fragile Five

TVP-VAR Frequency Connectedness Between the Foreign Exchange Rates of Non-Euro Area Member Countries

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