Risk and return spillovers among the G10 currencies

Abstract

We study spillovers among daily returns and innovations in the option-implied risk-neutral volatility and skewness of the G10 currencies. Using an empirical network model, we uncover substantial time variation in the interaction of returns and risk measures, both within and between currencies. We find that aggregate spillover intensity is countercyclical with respect to the federal funds rate and increases in periods of financial stress. Cross-currency spillovers of volatility and especially of skewness increase in times of stress, reflecting greater systematic risk. Similarly, in such times, returns become more sensitive to risk measures and vice versa.

On Tail Dependence and Multifractality

“…To better understand the changes for the symmetric and asymmetric cases, we present Figure 2 which shows ratios of multifractality measures between the symmetric or asymmetric tails and their parallel flat-correlations case to see how much the quantile-dependent auto-correlations influence the multifractal properties when compared to the standard autoregressive case. Note that linear correlation is simply an average correlation over all quantiles (this has been validated by auxiliary simulations as well) [45] so that, e.g., the symmetric quantile autoregression with auto-correlations of 0.9 at the upper and the lower quartile and no serial correlation for the bulk quartiles has a linear auto-correlation coefficient of 0.45, and in parallel, the asymmetric quantile autoregression with auto-correlations of −0.9 and 0.9 at their extremes and zeros at the bulk has a linear auto-correlation coefficient of zero. Again, we find several common patterns but also some clear distinctions between the methods.…”

Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets

“…Many of the papers surveyed in this study have focused on volatility transmissions across various regions and considering divergent markets. Worthy of mention is the European FX markets (see, e.g., Bub ak et al, 2011;Cai et al, 2008;Greenwood-Nimmo et al, 2016;Nikkinen et al, 2006), the International real estate market (see, e.g., Antonakakis et al, 2016;Caporin et al, 2019;Hoesli & Reka, 2011;Liow & Huang, 2018), International equity markets (see, e.g., Albulescu, 2020;Diebold & Yilmaz, 2009), major currency markets (see, e.g., Albulescu et al, 2019;Aslam et al, 2020;Salisu et al, 2018), cryptocurrency markets (see, e.g., Corbet et al, 2020;Lahmiri & Bekiros, 2020) among others. However, in recent times, we have noticed an emerging strand of literature on the impact of global pandemics on spillover transmissions between markets (see, e.g., Albulescu, 2020;Corbet et al, 2020;Youssef et al, 2006) with none having examined the contribution of COVID-19 to spillover transmissions among global currency pairs.…”

“…From a methodological perspective, different techniques have been adopted to capture volatility transmissions across markets. Some of the prominent techniques employed include; Vector Autoregression (VAR) (see, e.g., Greenwood-Nimmo et al, 2016;Nikkinen et al, 2006), TVP-VAR model (see, e.g., Youssef et al, 2006), Lagrange multiplier (LM) volatility spillover test (see, e.g., Nazlioglu et al, 2020), Bayesian Quantile-on-Quantile Approach (see, e.g., Caporin et al, 2019), Nonparametric Causality-in-Quantiles Test (see, e.g., Albulescu et al, 2019;Bahloul et al, 2018), Fourier Toda-Yamamoto causality (mean spillover) test (see, e.g., Nazlioglu et al, 2016), and most prominently Diebold and Yilmaz methodology (see, e.g., Diebold & Yilmaz, 2009;Liow & Huang, 2018;Salisu et al, 2018) to mention a few.…”

Spillovers and Asset Allocation

“…Early studies (e.g., Arshanapalli and Doukas 1993;Cheung and Ng 1996;Eun and Shim 1989;Hamao et al 1990;Lin et al 1994) mainly focus on the interconnectedness between the US stock market and other international stock markets, whereas subsequent studies extend the scope to regional stock markets such as Scandinavian (Booth et al 1997) or European markets (Bartram et al 2007), and to spillovers between spot and futures markets (Tse 1999). Significant efforts have also been devoted to examine other asset classes, including energy markets (Ji et al 2019;Rittler 2012;Xu et al 2019), credit markets (Collet and Ielpo 2018), commodity markets (Dahl and Jonsson 2018;Green et al 2018), bond markets (Reboredo 2018), or currency exchanges (Francq et al 2016;Greenwood-Nimmo et al 2016). Other studies examine asymmetric volatility spillovers, e.g., whether bad volatility spillovers dominate good volatility spillovers (Barndorff-Nielsen et al 2008;Baruník et al 2016;BenSaïda 2019;Xu et al 2019).…”