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Estimating financial institutions’ intraday liquidity risk: a Monte Carlo simulation approach

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  • Carlos León
Abstract
The most recent financial crisis unveiled that liquidity risk is far more important and intricate than regulation have conceived. The shift from bank-based to market-based financial systems and from Deferred Net Systems to liquidity-demanding Real-Time Gross Settlement of payments explains some of the shortcomings of traditional liquidity risk management. Although liquidity regulations do exist, they still are in an early stage of development and discussion. Moreover, no all connotations of liquidity are equally addressed. Unlike market and funding liquidity, intraday liquidity has been absent from financial regulation, and has appeared only recently, after the crisis. This paper addresses the measurement of Large-Value Payment System’s intraday liquidity risk. Based on the generation of bivariate Poisson random numbers for simulating the minute-by-minute arrival of received and executed payments, each financial institution’s intraday payments time-varying volume and degree of synchrony (i.e. timing) is modeled. To model intraday payments’ uncertainty allows for (i) overseeing participants’ intraday behavior; (ii) assessing their ability to fulfill intraday payments at a certain confidence level; (iii) identifying participants non-resilient to changes in payments’ timing mismatches; (iv) estimating intraday liquidity buffers. Vis-à-vis the increasing importance of liquidity risk as a source of systemic risk, and the recent regulatory amendments, results are useful for financial authorities and institutions.

Suggested Citation

  • Carlos León, 2012. "Estimating financial institutions’ intraday liquidity risk: a Monte Carlo simulation approach," Borradores de Economia 703, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:703
    DOI: 10.32468/be.703
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    References listed on IDEAS

    as
    1. World Bank, 2011. "Payment Systems Worldwide - A Snapshot : Outcomes of the Global Payment Systems Survey 2010," World Bank Publications - Reports 12813, The World Bank Group.
    2. Carlos León Rincón & Alejandro Reveiz, 2011. "Montecarlo simulation of long-term dependent processes: a primer," Borradores de Economia 8277, Banco de la Republica.
    3. Carlos Léon & Clara Machado & Freddy Cepeda & Miguel Sarmiento, 2011. "Too-connected-to-fail Institutions and Payments System’s Stability: Assessing Challenges for Financial Authorities," Borradores de Economia 644, Banco de la Republica de Colombia.
    4. Clara Machado & Carlos León & Miguel Sarmiento & Freddy Cepeda & Orlando Chipatecua & Jorge Cely, 2011. "Riesgo Sistémico Y Estabilidad Del Sistema De Pagos De Alto Valor En Colombia: Análisis Bajo," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(65), pages 106-175, June.
    5. Joaquin Bernal & Freddy Cepeda L. & Fabio Ortega C., 2011. "Cuantificación de la contribución de las fuentes de liquidez en el Sistema de Pagos de Alto Valor en Colombia: una aproximación preliminar," Borradores de Economia 683, Banco de la Republica de Colombia.
    6. Morten L. Bech, 2008. "Intraday liquidity management: a tale of games banks play," Economic Policy Review, Federal Reserve Bank of New York, vol. 14(Sep), pages 7-23.
    7. Kenneth R. French & Martin N. Baily & John Y. Campbell & John H. Cochrane & Douglas W. Diamond & Darrell Duffie & Anil K Kashyap & Frederic S. Mishkin & Raghuram G. Rajan & David S. Scharfstein & Robe, 2010. "The Squam Lake Report: Fixing the Financial System," Economics Books, Princeton University Press, edition 1, number 9261.
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    More about this item

    Keywords

    Payments Systems; Intraday; Liquidity Risk; Bivariate Poisson process; Monte Carlo Simulation; Liquidity Buffer; Oversight.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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