Intraday liquidity risk estimation using transaction data

Abstract

Intraday liquidity risk is a subject that applies to all banks, and arises whenever there is a timing mismatch between incoming and outgoing payments within a business day. In case such a mismatch occurs, the bank is exposed to the risk that it is unable to meet its payment obligations at the time expected. A liquidity buffer could help to mitigate this risk.

This thesis presents a framework for intraday liquidity risk management within ABN AMRO Bank, while taking different priorities of transactions into account. We examine the use of extreme value theory (EVT) and propose two metrics to capture the risk: the univariate and multivariate risk metric. The univariate risk metric represents the size of the liquidity buffer for each priority group separately and provides granular view. Making use of a Monte Carlo simulation algorithm in combination with univariate EVT, we
are able to estimate the size of the liquidity buffer for a specified time interval within a business day. We forecast the buffer size 30 days out-of-sample and test the violations against the conditional coverage (CC) hypothesis. Satisfactory results are obtained for the
groups with high and moderate priority when the highest confidence levels are considered: $\alpha$ = 0.1 and 0.05. For the group with low priority, the risk metric performs well for the lowest confidence levels: $\alpha$ = 0.025 and 0.01. The multivariate risk metrics aggregates
the size of the liquidity buffer, while taking the diversification of the priority groups into account. We define a failure set and investigate the use of multivariate EVT.