Market Liquidity Risk Limits

The risk measures used by a bank under stress scenarios should be estimated over a number of different time horizons.

Most risk managers in banks use a short time horizon, such as a day, which may be useful for day-to-day risk management. Along with that prudent risk managers will also estimate risk over longer horizons because the use of a short horizon such as one day assumes that market liquidity will always be sufficient to allow positions to be closed out at no or minimal losses. However, this doesn’t hold true during the times of crisis. In a crisis, market liquidity, or the financial institution’s access to markets, may be so impaired that it may become impossible to close out or hedge an existing position. Even if the institution is able to do it, it will have to do so at extremely unfavorable prices. In such a scenario the institution may have to hold positions for longer than they envisioned.

This unforeseen lengthening of the holding period will cause a portfolio’s risk profile to be much greater than envisioned in the original risk measure, as the likelihood of a large price change (volatility) increases with the horizon length. Additionally, the risk profiles of some instruments, such as options, change radically as their remaining time to maturity decreases.

Market makers should consider the bid-asked spreads in normal markets and potential bid-asked spreads in distressed markets and establish risk limits that consider the potential illiquidity of the instruments and products. Stress tests evidencing the “capital-at-risk” exposures under both scenarios should be available for examiner review. Market makers should consider placing limits on the size of concentrated positions relative to the market volume.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.