In this PhD thesis we develop tools for exploring liquidity risk within banks or within the banking network by using the data stored by financial market infrastructures. More specifically we study large value payment systems as they form the core of the whole financial infrastructure and make policy recommendations from a financial stability perspective. We develop an algorithm for the identification of money market trades that are present in the European large value payment system TARGET2. Further, we use the payment network to generate an LCR-like statistic on a daily basis and simulate liquidity failure of each of the systemically important banks. The aim of the chapter is to uncover paths of contagion which give a sense of potential systemic risk present in the network. Liquidity stress constitutes an ongoing threat to financial stability in the banking sector. For this reason, central banks carefully monitor the payment activities of banks in financial market infrastructures and try to detect early warning signs of liquidity stress. We investigate whether this monitoring task can be performed by supervised machine learning and find that it is a promising new tool.
|Qualification||Doctor of Philosophy|
|Award date||16 Sep 2020|
|Place of Publication||Tilburg|
|Print ISBNs||978 90 5668 628 4|
|Publication status||Published - 2020|