Liquidity Risk – Data Modeling and More


There are are a number of interesting book on liquidity model available if you happen to want to get your head into this space. A Quantitative Liquidity Model for Banks and Liquidity Modelling are two such book that are worth reading in my view. Robert Fielder’s book is definitely worth a read if your time is limited. So here goes in the usual format a few notes on Liquidity Modelling:

  • Page 12 – Assets and Obligations are the financial transactions of a bank with the outside world
  • Page 13 – Money has a time value, which nicely leads to cashflows (on which I have blogged many times before)
  • Page 15 – Net present value, the sum of the discounted cashflows of more than one instrument (e.g. portfolio)
  • Page 20 – Liquiditiy of a bank
  • Page 24 – Theoretical price, and spread
  • Page 28 – Value-liquidity-at-risk (VLaR)
  • Page 31 – 1bp, and the concept of PV01 or PVbp. Table 3.1 should be partially familiar to readers of my blog, as it, and the text on page 29 was covered in the various market risk postings I made some time ago. Throw in the scenarios for liquidity premium, and you end up with an m-dimensional distribution of profits/losses.
  • Page 40 – Future cashflows are all “predictions of payments” which may or may not occur based on a scenario. Each scenario has a set of assumptions, which provide input variables to the scenario functions and procedures. I think this is one of the key points to take away from reading this book
  • Page 42 – Table 4.1 provides some inputs to any UX individual who wants to think of visualization in the liquidity space – cashflows in, cashflows out, cashflow net, and Forward Liquidity Exposure (FLE).
  • Page 44 – Currencies are key, and risk-adjusted rates now come into play
  • Page 57 – Continues the nice example of IRS and the Forward rate and Forcast-at-risk inputs.
  • Page 67 – Barings example
  • Page 6x – Hypothetical transaction modeling, liquidity/breach/rejectable options and the counterparty effect
  • Chapter 6 – Discusses a framework for capturing relevant data on a banks liquidity risk. As the author states, all banks a different, so following “best practices” isn’t the complete solution. It’s also important to remember that building a system before you understand what needs to be modeled is going to cause issues (page 115). I suspect some of this modeling may also have a play from a bitemporal angle given the future scenarios discussed throughout the chapters.
  • Chapter 7 – Counterbalancing Capacity (CBC)
  • Chapter 8 – Intra-day Liquidity Risk (ILR). Nostro, vostro/loro accounts and payments are covered in this chapter which make for an interesting read if you’re unfamiliar with payments – payment agents, TARGET (Transeuropean Automated Real Time Gross Settlement Express Transfer) and EBA (Euro Banking Association).
  • Page 179 – Figure 8.4 provides a nice time line of deals, cashflows and payments. From a modeling perspective this could be done by either references within the data model. Alternatively, a bitemporal data model maybe appropriate.
  • Page 242 – Liquidity Risk Limits – yet another limit to consider in the course of the trading day
  • Chapter 10 – Basel III. Total net cashflows (TNCOs) of the first 30 calendar days, coupled with high-quality liquid assets (HLAs – level 1 and 2). This chapter discusses a number of LCR improvements strategies for banks

In summary, an excellent book to read on liquidity risk, providing background for anyone working on the data modeling/architectures of such systems.

~ by mdavey on March 5, 2012.

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