Measuring Market Liquidity Risk - Which Model Works Best?

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Market liquidity risk, the diculty or cost of trading assets in crises, has been recognized as an important factor in risk management. Literature has already proposed several models to include liquidity risk in the standard Value-at-Risk framework. While theoretical comparisons between those models have been conducted, their empirical performance has never been benchmarked. This paper performs comparative back-tests of daily risk forecasts for a large selection of traceable liquidity risk models. In a 5.5 year stock sample we show which model provides most accurate results and provide detailed recommendations which model is most suitable in a specic situation. Terms of use: Documents in EconStor may

The impact of the French Tobin tax

“…The implicit idea is that the effective spread is an estimate of the execution cost paid by the trader and the gross revenue earned by the liquidity provider. Unfortunately, we do not possess order-level data, and, as pointed out by Ernst et al (2009), the best liquidity measures are based on limit order book data. Thus, we follow the Corwin and Schultz (2012) methodology to estimate the bid-ask spread.…”

Liquidity Adjusted Value at Risk: Integrating the Uncertainty in Depth and Tightness

“…The first exogenous approach is proposed by Bangia et al (1999) and based on the tightness dimension of liquidity as measured by bid-ask spreads. A similar model by Ernst et al (2012), modified the assumption regarding spread distribution by Cornish-Fisher expansion which improved the accuracy of forecasting. Exogenous models assume that no single investor could have any impact on the current market price.…”

“…More importantly, even though the computer technology is recently capable of handling large datasets, majority of these models are less straightforward to be implemented by finance practitioners, either due to time or hardware limitations or complexity or to some extent due to data availability/quality. On the other hand, exogenous models are straightforward in implementation and interpretation but come with a simplifying and often criticized assumption, implying a perfect competition in market, whereby no single investor would have significant impact on the market price (Bangia et al 1999;Stange and Kaserer, 2008;Ernst et al 2012).…”

Loan-to-value ratio for inventory financing loan under structural breaks

“…On this basis, Francois-Heude and Van Wynendaele (2001) [2] put forward the volume-weighted bid-ask spread model and took into account all the purchase price, the selling price and the trading volume realized, which make a more accurate calculation of the liquidity. Ernst et al (2009) [3] had amended the above-mentioned model and proposed volume weighted average spread model in non-normal distribution. In addition, there are also liquidity models on the basis of transaction price and volume such as transaction regression model (Berkowitz, 2000) [4] and price-based volume liquidity model (Cosandey, 2001) [5]).…”

“…But no scholars further study loan-to-value rate model with SB of volatility and liquidity risk of pledged inventory financing business. Compared to the existing literature, this study focus on loan to-value rate of pledged inventory in the framework of SB of volatility and VaR incorporated liquidity risk with survey data from China Eastern Silk Market and on the basis of drawing on results of Ernst et al (2009), and Z. C. Wang (2003).…”