This paper applies the PageRank algorithm, often used in network analysis, to capture multidimensional and high-degree, cross-border banking relations among countries. It provides a nuanced picture of financial interconnectedness that has not been available in the literature to date.Our measure, FIRank, shows the probability of connection to the network by any country or, equivalently, the share of all connections captured by each country, and provides relative rankings of countries according to their degree of interconnectedness. We show that the United Kingdom and the United States remain the ‘core’ in the global banking network over a thirty-three-year period, with most countries scattered in the ‘periphery’ despite considerable growth and change in the network.This finding contrasts with claims of an increasingly even distribution of connections reported in other studies using more limited network measures. Examining whether financial interconnectedness raises or lowers output volatility, we show that the relationship is nonlinear: initially, higher interconnectedness raises volatility, but beyond a critical level volatility is reduced. This is true in periods of smaller and idiosyncratic shocks but is even more pronounced in the GFC period of large shocks. The novelty of our approach lies in applying well-understood network measures to cross-border banking data to identify where countries rank in international financial interconnectedness with the global bank-lending network.Further, by explicitly analysing how the relative interconnectedness index is related to output volatility we provide new insights into the pros and cons of higher levels of international financial interconnection.

本文采用了经常在网络分析中使用的PageRank算法，以捕获国家之间的多维，高度，跨境银行关系。它提供了迄今为止文献中尚无的金融互连性的细微差别图片。我们的度量FIRank显示了任何国家/地区与网络建立连接的可能性，或者等效地显示了每个国家/地区获得的所有连接的份额，以及提供根据国家的相互联系程度的相对排名。我们显示，在过去的33年中，英国和美国仍然是全球银行网络的“核心”，尽管网络的增长和变化很大，但大多数国家仍分散在“外围”中。这一发现与其他研究报告中使用有限网络措施的连接分布越来越均匀的说法形成鲜明对比。检查金融互连性是提高还是降低产出波动率，我们发现这种关系是非线性的：最初，较高的互连性会提高波动率，但超过临界水平时，波动率会降低。在较小且特殊的震荡时期确实如此，但在大震荡的GFC时期更为明显。我们方法的新颖之处在于，将易于理解的网络措施应用于跨境银行数据，以识别各国在与全球银行贷款网络之间的国际金融互连方面的排名。