Estimating Network Connectedness of Financial Markets and Commodities
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ISSN: 1004-3756 (paper), 1861-9576 (online) CN 11-2983/N
Estimating Network Connectedness of Financial Markets and Commodities Ehsan Bagheri, Seyed Babak Ebrahimi Financial Engineering Group, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran [email protected], [email protected] ()
Abstract. We investigate the directional volatility and return network connectedness among stock, commodity, bond, currency and cryptocurrency markets. The period of study covers Feb 2006 until August 2018. We utilize and expand Diebold and Yilmaz (2014 2015) connectedness measurement; accordingly, in the variance decomposition structure, we use Hierarchical Vector Autoregression (HVAR) to estimate high dimensional networks more accurately. Our empirical results show that markets are highly connected, especially during 2008-2009. Asian stock markets are the net receiver of shocks, while European and American stock markets are the net transmitter of shocks to other markets. The pairwise connectedness results suggest that among stock markets, DAX-CAC 40, FTSE 100-CAC 40 and S&P 500-S&P_TSX index are more integrated through connectedness than the others. For other markets, WTI crude oil - Brent crude oil, 30-Year bond and 10-Year bond, Dollar Index futures-EUR/USD have notable connections. In terms of cryptocurrencies, they contribute insignificantly to other markets and are highly integrated with each other. Gold and cryptocurrencies seem to be good choices for investors to hedge during a crisis. Keywords: Financial markets, network, connectedness, econometrics, commodity
1. Introduction The connectedness between different stocks and markets and how they are networked has been the subject of many studies (see Diebold and Yilmaz 2012 2014 2015, Ebrahimi and Seyedhosseini 2015, Xu and Tang 2018, Mensi et al. 2018a, Abbas et al. 2019). From a macro perspective, after the financial crisis of 2008 and the European sovereign debt crisis, many policymakers found concerns about the connectedness of markets and the harmful effects on the economy (Jeong and Park 2018). From a micro perspective, portfolio managers and investors need to know how to achieve higher diversification and choose the best hedge strategies to manage risk (Boon and Ielpo 2014). The purpose of this study is to investigate the directional volatility and return network connectedness among stock, commodity, bond, currency, and cryptocurrency markets from Feb 2006 to August 2018. The primary objective is to measure the
connectedness of all market levels from pairwise to system-wide. We aim to utilize and expand Diebold and Yilmaz (2012 2014) methodology which is based on Generalized Variance Decomposition (GVD) and to estimate the connectedness of different markets using Hierarchical Vector Autoregression (HVAR) introduced by Nicholson et al. (2018). HVAR optimizes high dimensionality and embeds the notion of lag selection into a convex regularize (Nicholson et al. 2018). In addition, we consider both static and rolling network connected
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