Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through October 2008. We show that despite significant volatility fluctuations in all markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis that began in 2007. As the crisis intensified, so too did volatility spillovers, with particularly important spillovers from the bond market to other markets. JEL classification numbers: G1, F3

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities. We also show that variance decompositions define weighted, directed networks, so that our connectedness measures are intimately-related to key measures of connectedness used in the network literature. Building on these insights, we track both average and daily time-varying connectedness of major U.S. financial institutions' stock return volatilities in recent years, including during the financial crisis of 2007-2008. Terms of use: Documents in EconStor may

We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers; both turn out to be empirically important. In particular, in an analysis of 19 global equity markets from the early 1990s to the present, we find striking evidence of divergent behaviour in the dynamics of return spillovers vs. volatility spillovers: return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts.For many years but especially following the late 1990s Asian crisis, much has been made of the nature of financial market interdependence, both in terms of returns and return volatilities (King et al., 1994;Forbes and Rigobon, 2002). Against this background, we propose a simple quantitative measure of such interdependence, which we call a spillover index, and associated tools that we call spillover tables and spillover plots.The intensity of spillovers may of course vary over time and the nature of any timevariation is of potentially great interest. We allow for it in an analysis of a broad set of global equity returns and volatilities from the early 1990s to the present and we show that spillovers are important, spillover intensity is indeed time-varying and the nature of the time-variation is strikingly different for returns vs. volatilities.We proceed by proposing the spillover index in Section 1 and describing our global equity data in Section 2. We perform a full-sample spillover analysis in Section 3 and a rolling-sample analysis allowing for time-varying spillovers in Section 4. We briefly assess the robustness of our results in Section 5 and we summarise and conclude in Section 6. The Spillover IndexWe base our measurement of return and volatility spillovers on vector autoregressive (VAR) models in the broad tradition of Engle et al. (1990). Our approach, however, is very different. We focus on variance decompositions, which are already well understood and widely calculated. As we show, they allow us to aggregate spillover effects across markets, distilling a wealth of information into a single spillover measure.The basic spillover index idea is simple and intuitive, yet rigorous and replicable, following directly from the familiar notion of a variance decomposition associated with an N-variable VAR. Roughly, for each asset i we simply add the shares of its forecast

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What accounts for the worldwide advance of financial reforms in the last quarter century? Using a new index of financial liberalization, we find that influential events shook the policy status quo. Balance-of-payments crises spurred reforms, but banking crises set liberalization back. Falling global interest rates strengthened reformers, while new governments went both ways. The overall trend toward liberalization, however, reflected pressures and incentives generated by initial reforms that raised the likelihood of additional reforms, stimulated further by the need to catch up with regional reform leaders. In contrast, ideology and country structure had limited influence.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statistically, we find that global banking connectedness is clearly linked to bank location, not bank assets. Dynamically, we find that global banking connectedness displays both secular and cyclical variation. The secular variation corresponds to gradual increases/decreases during episodes of gradual increases/decreases in global market integration. The cyclical variation corresponds to sharp increases during crises, involving mostly cross-country, as opposed to within-country, bank linkages. Terms of use: Documents in

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