The liquidity risk premium continues to pose challenges to financial modellers and practitioners, and liquidity risk is one of the more difficult financial metrics to model and manage.
The liquidity risk premium is the compensation required by providers of liquidity, and is likely to vary across markets and over time. It also plays a role in collateralized borrowing terms – lenders may need to liquidate collateral, and they are likely to demand larger collateral buffers or higher borrowing rates if they accept illiquid assets as security.
After the LTCM collapse in 1998, Myron Scholes identified an industry need for ‘liquidity options’ – effectively an insured credit line, where the option price includes a liquidity premium for a set of trading positions and collateral.
During the 2008 financial crisis, it was often said that the issue for the banks was one of liquidity, not solvency; and Central Banks now provide a form of Scholes’ ‘liquidity option’, but only to systemically important banks and financials.
Anyone else who needs to raise cash quickly by selling assets is likely to face a substantial discount. To estimate this, traders sometimes use the approximation: “adjust the price by one days’ volatility in order to trade one days’ volume”.
This is very useful for traded assets like equities and frequently traded bonds, where volume data is available; but many bonds, CDS and secondary loans are notoriously illiquid. As a result, an entire industry has developed to provide evaluated and model prices for bonds and other instruments that do not trade regularly. To estimate the realistic price at which a bond or CDS would trade, it is necessary to know the appropriate liquidity risk premium for that asset.
This requires an understanding of the proportion of an observed credit-risky yield that is liquidity-driven, by decomposing the credit spread into credit and liquidity components.
There is a large academic literature on this issue, which generally suggests that the liquidity risk premium is partly a function of instrument maturity (where there is some compensation for potential, future illiquidity) as well as credit category.
This academic literature has been somewhat constrained by a lack of data. But crowd-sourced data from global IRB banks is now available to provide estimated real world probabilities of default. This data can be used to estimate the various liquidity risk premiums that permeate markets, with practical applications that include benchmarks for Valuations and CVA, Collateral Management and CECL/IFRS9 impairment estimates.
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