Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Abstract

We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectedness, we obtain interesting results. We show that divergence in monetary policy regimes affects forex volatility spillovers but that adding oil to a forex portfolio decreases the total connectedness of the mixed portfolio. Asymmetries in connectedness are relatively small. While negative shocks dominate forex volatility connectedness, positive shocks prevail when oil and forex markets are assessed jointly. Frequency connectedness is largely driven by uncertainty shocks and to a lesser extent by liquidity shocks, which impact long-term connectedness the most and lead to its dramatic increase during periods of distress.

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

“…In order to make our results robust and consistent, we employ another methodology, namely frequency connectedness, as proposed by Baruník and Kocenda (2019), to capture the interconnectedness as well as the spillover effect for significant Transfer Entropy values in Table 4 and Table 5. However, this method is based on balanced data; thus, we have the sub-sample from 2016 to 2019 for our variables.…”

Financialisation of natural resources & instability caused by risk transfer in commodity markets

“…We employed the interconnectedness method to capture the spillovers and volatility transmission, devised by Diebold & Yilmaz (2009) as well as Baruník & Kocenda (2019). The fundamental concept is based on variance decomposition, extracted in the estimates of Vector Auto-Regressions (VAR).…”

Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty

“…In order to investigate the spillover effects and connectedness for both returns and volatility of nine US dollar exchange rates, we follow Diebold and Yilmaz (2012) , and Diebold and Yılmaz (2014) to specify a generalized VAR model where exchange rate returns and volatility are alternatively used as dependent variables along with the TPU returns and volatility. This empirical approach allows us to generate the total spillover index based on the H-step ahead generalized forecast error variance decomposition (GFEVD) and to assess the degree of connectedness based on the directional spillover index ( Baruník & Kočenda, 2019 ) 2 because the approach of Baruník and Kočenda (2019) accounts for the spectral representation of variance decompositions for high-frequency data. Therefore, we also consider the aforementioned approach to estimate for the spillover effects and connectedness in this study.…”

“…In addition, and are selection errors with one as the jth and kth element and zeros otherwise. We follow the approach of Baruník and Kočenda (2019) to measure directional spillovers from exchange rate j to exchange rate k as follows: …”