Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets.

• Author: Barunik, Jozef

1. INTRODUCTION

   Knowledge and quantification of the volatility connectedness, or volatility spillovers, between oil and forex markets is important because most crude oil production and sales is quoted and invoiced in US dollars (Devereux et al., 2010), and oil prices in domestic currencies thus depend substantially on the dollar exchange rate. (1) Payments for the oil sold on the market represent massive financial flows entering the forex market (Baker et al., 2018). In addition, large financial flows come from financial players with no interest in oil as a physical commodity, which contributed to the spectacular increase in the financialization of oil after 2004 and the subsequent reshaping of the oil market (Fattouh and Mahadeva, 2014). (2) Empirical evidence also shows that oil prices possess predictive power with respect to the exchange rates of the oil-exporting countries (Ferraro et al., 2015).

   In connection to the above phenomena, there is considerable potential for the uncertainty (i.e., volatility) in oil prices to transfer into the uncertainty of foreign currencies on the forex market and vice versa. As such, large volatility spillovers are likely to emerge between the two markets. The literature analyzing the nexus between oil and forex markets has investigated primarily co-movements between the two. However, to the best of our knowledge, volatility spillovers between the two markets have not been fully explored yet despite that they impact many areas of research and carry practical implications related to risk management (Kanas, 2001), portfolio allocation (Aboura and Chevallier, 2014), and business cycle analysis. Hence, our goal and contribution is a comprehensive analysis of the volatility connectedness between the oil and forex markets.

   We analyze three types of connectedness, and each direction is supported by specific motivation. First, total connectedness makes it possible to quantify the aggregate extent of volatility spillovers between the two markets. The oil market exhibits historically high volatility that also surpasses that of other energy commodities (Regnier, 2007). Oil price volatility is particularly important because it represents risk to producers and industrial consumers in terms of production, inventories, and transportation, and it also affects the decisions of purely financial investors (Pindyck, 2004) and decisions on strategic investments (Henriques and Sadorsky, 2011). The sheer extent of transactions related to oil is likely to produce substantial volatility spillovers. However, their extent might change when combined with the relevant forex transactions. Hence, this part of our analysis enables us to assess how the aggregate level of connectedness between the two markets evolves. We also relate dynamics in the total connectedness to major economic conditions and events that affect both types of studied assets. We analyze the aggregate connectedness between the oil and forex markets with the volatility spillover index (the DY index) of Diebold and Yilmaz (2009) that was further improved in Diebold and Yilmaz (2012).

   Second, we extend our analysis to account for potential asymmetries in connectedness. Narayan and Narayan (2007) show that negative and positive shocks produce asymmetric effects on oil price volatility. Further, Kilian (2009) shows that demand shocks related to the potential future shortfalls in oil supply affect oil prices more than actual physical supply shocks do. Since oil is an asset for which spillovers have historically played a prominent role (Haigh and Holt, 2002), the existence of asymmetries in oil price volatility might naturally lead to asymmetries in volatility spillovers. Subsequently, currencies on the forex market would be able to continuously absorb or transfer those asymmetries because of the 24-hour operation of the global forex market. Moreover, Barunik et al. (2017) show that currencies exhibit asymmetric connectedness. These asymmetries might be transferred via the US dollar or other key currencies, as the forex market exhibits a very high degree of integration, especially for the key currencies (Kitamura, 2010). We account for asymmetric sources of volatility by computing the DY index with the realized semivariances following Barndor-Nielsen et al. (2010). Barunik et al. (2016) combined the DY index with realized semivariances and produced a flexible measure allowing for dynamic quantification of asymmetric connectedness. Third, we assess frequency connectedness to distinguish whether connectedness is formed at shorter or longer frequencies, i.e., shorter or longer investment horizons. Barunik and Kfehlik (2018) argue that shocks to economic activity impact variables at various frequencies with various strengths, and to understand the sources of connectedness in an economic system, it is crucial to understand the frequency dynamics of connectedness. The key reason is that agents operate on different investment horizons--these are associated with various types of investors, trading tools, and strategies that correspond to different trading frequencies (Gencay et al., 2010; Conlon et al., 2016). Shorter or longer frequencies are the result of the frequency-dependent formation of investors' preferences, as shown in the modeling strategies of Bandi and Tamoni (2017); Cogley (2001); Ortu et al. (2013). In our analysis, we consider the long-, medium-, and short-term frequency responses to shocks and analyze financial connectedness at a desired frequency band. We compute frequency connectedness based on the approach of Barunik and Kfehlik (2018).

   The remainder of the paper is organized as follows. In Section 2, we provide an overview of the literature related to volatility spillovers on the oil and forex markets. In Section 3, we formally introduce the methodological approach. The data are described in Section 4. In three separate subsections of Section 5, we present our results for total, asymmetric, and frequency connectedness. Conclusions are offered in Section 6. Finally, supplementary material to this paper (available at https://doi.org/10.5547/01956574.40.SI2.jbar) provides additional information that we will refer to as supplementary material in the rest of the text.

2. LITERATURE REVIEW

   The growing oil market has become the world's largest commodity market, and oil trading has been transformed from a primarily physical product activity into a sophisticated financial market (Manera, 2013). However, the research related to volatility spillovers among oil-based commodities is limited. Haigh and Holt (2002) analyze the effectiveness of crude oil, heating oil, and unleaded gasoline futures in reducing price volatility for an energy trader and show that uncertainty is reduced significantly when volatility spillovers are considered in the hedging strategy. Hammoudeh et al. (2003) analyzed the volatility spillovers of the same three major oil commodities and showed the impact of different trading centers. Lin and Tamvakis (2001) found substantial spillover effects when the two major markets for crude oil (NYMEX and London's International Petroleum Exchange) are trading simultaneously. Chang et al. (2010) found volatility spillovers and asymmetric effects across four major oil markets: West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific).

   Restrepo et al. (2018) analyze the spillover effect of stock markets' VIX on crude oil and document large similarities in the correlation dynamics between the crude oil and stock volatility series. Barunik et al. (2015) quantify volatility spillovers among crude oil, gasoline, and heating oil and show that asymmetries in overall volatility spillovers due to negative (price) returns materialize to a greater extent than those due to positive returns. Their occurrence also frequently indicates the extent of real or potential crude oil unavailability, which is in line with the arguments of Kilian (2009).

   Analyses of forex volatility spillovers based on the DY index remain rare. (Diebold and Yilmaz, 2015, Chapter 6) analyze the exchange rates of nine major currencies with respect to the U.S. dollar from 1999 until mid-2013. They show that forex market connectedness increased only mildly after the 2007 financial crisis and that the euro/U.S. dollar exchange rate exhibits the highest volatility connectedness among all analyzed currencies. Greenwood-Nimmo et al. (2016) generalize the connectedness framework and analyze risk-return spillovers among the G10 currencies between 1999 and 2014. They find that spillover intensity is countercyclical and that volatility spillovers across currencies increase during crisis periods. Similarly, Bubak et al. (2011) document statistically significant intra-regional volatility spillovers among the European emerging forex markets and show that volatility spillovers tend to increase in periods characterized by high market uncertainty. Further, McMillan and Speight (2010) and Antonakakis (2012) document the existence of volatility spillovers among the exchange rates of major currencies. Finally, Barunik et al. (2017) document sizable asymmetries in volatility spillovers among the most actively traded currencies.

   The recent connection between the oil and forex markets was identified by Aloui et al. (2013), who analyze the dependence structure between crude-oil spot prices (WTI Cushing and Brent price indices) and nominal exchange rates of the U.S. dollar against five major currencies (euro, Canadian dollar, British pound, Swiss franc, and Japanese yen). Their results reveal the existence of a dependence structure between the two markets over the 2000-2011 period along with a significant and symmetric dependence for almost all analyzed oil-exchange rate pairs. An increase in the price of oil is found to be associated with the depreciation of the dollar. The results resonate well with the earlier findings shown for the period...