Bank funding liquidity and market liquidity failures are serious economic events that can cause devastating effects within an economy. The most recent example of this has been the 2008 global financial crisis when unregulated financial operations absorbed high levels of risk which ultimately resulted in economic catastrophe. The relevance of high liquidity risks in the most recent crisis is reflected in the fact that interbank markets collapsed globally and individual central banks had no choice but to intervene extraordinarily in money markets. One of the reasons funding liquidity and market liquidity risks skyrocketed and went unnoticed before the crisis was the lack of measurements available to quantify these risks, which made firms, citizens, and government enterprises unaware of the implications of all their economic choices leading up to the financial crisis. To avoid another economic disaster of this sort, it is important to improve the regulation risk-taking, starting with quantifiable risk measures.
Mathias Drehmann and Kleopatra Nikolaou have tackled this issue with a measure of funding liquidity that can be used to represent current and future market conditions. They define bank funding liquidity as the ability of a bank to settle obligations immediately when they are due. This entails that a banking establishment has capital inflows be greater than or equal to capital outflows at any given point in time. They note that the factors that influence institutions funding liquidity risk include the time horizon considered, the nature of the funding position of the bank, future random inflows and outflows of assets and future random prices of obtaining liquidity from various funding sources. These random prices of obtaining liquidity are interest rates; in particular, the rate at which banks can borrow funds from the central bank, which acts as the lender of last resort. Based on these premises, Drehmann and Nikolaou derived a mathematical model of the level of funding liquidity risk a bank takes on, which takes the form of [((Bid-rate)-(policy rate))*volume]/(total allotment). The bid rate refers to the interest rate banks pay to the Fed based on Term-Auction Facilities, which since the financial crisis has been used to auction Federal Reserve funds to banks who bid the highest for them. The policy rate refers to the Fed’s target interest rate, while volume represents the quantity being borrowed by a specific bank at the bid rate. Total allotment represents all the federal funds used to support funding liquidity, and thus converts a bank’s use of Fed funding as a proportion of total funding for all banks. Because the bid rate and the volume show indicate the desperation of a bank for loans, they reflect what extent risky behavior surfaces in a bank’s transactions.
This measure of funding liquidity may be the best available to date, based on the data it uses to make deductions. The variables used in the formula are, in most countries, publicly available and regularly updated. This enables indexing of risk to occur on a regular basis, and transparency via the non-confidentiality of the numbers involved. It also standardizes numbers from individual banks based on the entire status of the banking system. This measure also coincides with the standard theory that funding liquidity risk is highly correlated with market liquidity risk, especially in times of economic downturns. Drehmann and Nikolaou demonstrated this correlation in an empirical study of funding liquidity and market liquidity data from June 2005 to December 2007 in the euro area, the results of which can be seen in Figure 1. Market liquidity is defined as the difference between transaction prices and fundamental values in a market. Put in simpler terms, market liquidity is the ease at which transactions are made; if there are barriers to trade, then transaction costs will increase the prices of goods and services to reflect the difficulty in obtaining them.
The correlation between funding liquidity risk and market liquidity risk is not one to be considered lightly, as it has many implications in terms of liquidity spirals. Brunnermeier and Pedersen report that market liquidity risk can increase for several reasons, including shock to speculators’ capital and a high level of difficulty in assessing specific fundamental values. In general, anything that causes negative speculation about the future or minimized funding in a market may increase market liquidity risk. Many variables are available to measure market openness and ease, including tax codes, trade regulations, transaction volumes, and transaction costs. As speculative outlooks become sour or obstacles threaten economic progress at consistent or increasing rates, employers may be less likely to hire, entrepreneurs may be less likely to invest, and consumers may be less likely to consume in fear of future economic downturn. Thus, herein lies the liquidity trap created by the combination of high funding liquidity risk and high market liquidity risk. When a shock or pattern causes one to be high, the result is the other one will rise as well. This cycle is demonstrated in Figure 2. This flow diagram exhibits how high funding liquidity risk or high market liquidity risk (low market liquidity) exacerbates the other because the consequences of one become the fuel of the other. This liquidity spiral represents the mechanisms through which a liquidity trap was reached in the most recent global crisis. In order to avoid the problems associated with high risk and liquidity traps, it is essential to continue developing more accurate measurement tools so that funding liquidity and market liquidity risks can be regulated more efficiently in the future to avoid the financial problems that may come with them.
(Figures 1 & 2 omitted for preview. Available via download)
Markus K. Brunnermeier & Lasse Heje Pedersen, Market Liquidity and Funding Liquidity (NBER Working Paper No. 12939, 2007).
Mathias Drehmann & Kleopatra Nikolaou, Funding Liquidity Risk: Definition and Measurement (Journal of Banking & Finance: Vol. 37, Issue 7, 2013), 2173-2182.