Fear connectedness among asset classes

ABSTRACT

This study investigates the interconnection between five implied volatility indices representative of different financial markets during the period 1 August 2008–29 December 2017. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yilmaz. Second, we make use of a dynamic analysis to evaluate both the net directional connectedness for each market and all net pairwise directional connectedness. Our results suggest that a 38.99%, of the total variance of the forecast errors is explained by shocks across markets, indicating that the remainder 61.01% of the variation is due to idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability. Finally, we also document frequently switch between a net volatility transmitter and a net volatility receiver role in the five markets under study.

KEYWORDS:

• Implied volatility indices
• financial market linkages
• connectedness
• VAR
• variance decomposition

JEL CLASSIFICATION:

• Codes: C53
• E44
• F31
• G15

Acknowledgements

The authors wish to thank two anonymous referees and the editor for their helpful comments and suggestions on a previous draft of this article, which have enabled us to introduce substantial improvements. Julián Andrada-Félix gratefully acknowledges warm hospitality and financial support of the Department of Finance at the Auckland University of Technology during his research visit. Simón Sosvilla-Rivero thanks the members of the Department of Economics at the University of Bath for their warm hospitality during his research visit.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ For excellent primers on the VIX, see Whaley (2009) and Gonzalez-Perez (2015).

² Poon and Granger (2003) concluded that the VIX is the best predictor of realized volatility, although it may be a biased one.

³ The connectedness methodology has several advantages over the alternative approach of focusing on contemporaneous correlations (corrected or not for volatility). First, while correlation is a symmetrical measure, connectedness is an asymmetrical one, so the procedure provides information on the direction and magnitude of the volatility transmission (from country A to country B, from country B to country A, or both). Second, by investigating dynamic connectedness through a rolling window, we can evaluate how the strength of the connectedness evolves over time, allowing us to detect episodes of sudden and temporary increases in volatility transmission.

⁴ Awartania, Maghyerehb, and Al Shiabc (2013), Lee and Chang (2013), Chau and Deesomsak (2014) and Cronin (2014) apply this methodology to examine spillovers in the US markets; Yilmaz (2010), Zhou, Zhang, and Zhang (2012) and Narayan, Narayan, and Prabheesh (2014) focus on Asian countries; Apostolakisa and Papadopoulos (2014) and Tsai (2014) examine G-7 economies; Demirer et al. (2015) estimate global bank network connectedness and Diebold and Yilmaz (2016) characterize equity return volatility connectedness in the network of major American and European financial institutions; McMillan and Speight (2010), Antonakakis (2012) and Bubák, Kocenda, and Zikes (2014) examine interdependence and spillovers in exchange rate markets; and Antonakakis and Vergos (2013), Alter and Beyer (2014), Claeys and Vašícek (2014) and Fernández-Rodríguez, Gómez-Puig, and Sosvilla- Rivero (2016) use connectedness analysis to assess financial stress transmission in European sovereign bond markets.

⁵ Since its introduction in 1993, the VIX Index has been considered to be the world’s premier barometer of investor sentiment and market volatility. The VIX has been utilized as a proxy for the level of investor risk aversion or market sentiment (see, e.g., Brunnermeier, Nagel, and Pedersen 2008; or Bekaert, Hoerova, and Duca 2013).

⁶ Recall that option prices provide a unique insight into the probabilities assigned by markets to various future outcomes for a particular economic variable.

⁷ Note that gold is a precious and highly liquid metal, so it is categorized as a commodity and a monetary asset. Gold has possessed similar characteristics to money in that it acts as a store of wealth, medium of exchange and a unit of value (Goodman 1956; Solt and Swanson 1981). Gold has also played an important role as a precious metal with significant portfolio diversification properties (Ciner 2001).

⁸ See http://www.cboe.com/micro/vix/vixwhite.pdf.

⁹ All results are based on VARs of order 2 and GVDs of 10-day ahead volatility forecast errors. To check for the sensitivity of the results to the choice of the order of VAR, we also calculate the spillover index for orders 2 through 4, as well as for forecast horizons varying from 4 to 10 days. The main results of our article are not affected by these choices. Detailed results are available from the authors upon request.

¹⁰ During this period, there was the 6 May 2010 Flash Crash, one of the most turbulent periods in the history of financial markets.

Additional information

Funding

This article is based on work supported the Auckland University of Technology [VRF2016-12], the Bank of Spain [grant PR71/15-20229], the Spanish Ministry of Education, Culture and Sports [grant PRX16/00261] and the Spanish Ministry of Economy and Competitiveness [grant ECO2016-76203-C2-2-P];Auckland University of Technology, New Zealand [VRF2016-12];