Collateral re-use: unveiling the risk of delivery failures and higher volatility in the repo market

Miruna-Daniela Ivan

The widespread practice of financial institution to re-use securities received as collateral plays a key role in the repurchase agreement (repo) market functioning. By increasing the availability of securities which can be used as collateral, collateral re-use lowers funding costs under normal market conditions, allowing collateral to flow to where it is most needed. But this activity may amplify the risk of delivery failures and increase volatility in repo rates during periods of market stress. This article explores the level of collateral re-use in the gilt repo market, applying algorithms from academic literature to the Bank’s Sterling Money Market Data, and provides supporting evidence of collateral re-use procyclicality, and its positive relation to repo rates volatility.

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Could digitalisation of finance lead to more disruptive international capital flows?

Simon Whitaker

Digital currencies and the tokenisation of financial assets could speed up the movement of money and assets between institutions and across borders. Historically, the liberalisation of capital flows led to debates about the impact on macroeconomic and financial stability. Bouts of instability – for example the 2008 global financial crisis – provoked calls to put ‘sand in the wheels’ of financial markets. In this blog I argue there is no reason why lubricating capital flows through digitalisation should herald a new era of financial instability. But the architecture of the global financial safety net may need to evolve to contain risks to the international monetary and financial system.

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Boosted inflation – using machine learning to make sense of non-linear determinants of inflation

Marcus Buckmann, Galina Potjagailo and Philip Schnattinger

Disentangling the sources of high inflation, exceeding inflation targets in the post- pandemic period, has been a priority for monetary policy makers. We use machine learning for this task – a boosted decision tree model that fits non-linear associations between many indicators and inflation. We add economic interpretability by categorising the data into intuitive blocks representing components of the Phillips curve. To further disentangle inflation drivers, we separate the signals that reflect demand and supply by imposing sign-restrictions on the decision trees. Our model tells us that both global supply and domestic demand spurred UK CPI inflation post-pandemic. We detect important non-linearities: in the Phillips curve relationship with labour market tightness and unemployment and via additional effects from short-term inflation expectations.

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