How large is liquidity risk in an automated auction market?

Giot, Pierre; Grammig, Joachim

doi:10.1007/978-3-7908-1992-2_6

Cited by 4 publications

(12 citation statements)

References 27 publications

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“…The TaR is sometimes interpreted as a 'liquidity' risk measure since the length of intra-trade durations reveals the speed of activity. However, it does not allow us to predict the potential price impact of liquidating a portfolio, which is accounted for, for instance, by the so-called 'actual VaR' proposed by Giot and Grammig 2006. Besides, liquidity levels and systematic liquidity are priced in asset returns, and liquidity risk can thus be considered as incorporated into prices (market risk).…”

Section: High-frequency Risk Measuresmentioning

confidence: 99%

Forecasting High‐Frequency Risk Measures

Banulescu

Colletaz

Hurlin

et al. 2015

Journal of Forecasting

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This article proposes intraday high‐frequency risk (HFR) measures for market risk in the case of irregularly spaced high‐frequency data. In this context, we distinguish three concepts of value‐at‐risk (VaR): the total VaR, the marginal (or per‐time‐unit) VaR and the instantaneous VaR. Since the market risk is obviously related to the duration between two consecutive trades, these measures are completed with a duration risk measure, i.e. the time‐at‐risk (TaR). We propose a forecasting procedure for VaR and TaR for each trade or other market microstructure event. Subsequently, we perform a backtesting procedure specifically designed to assess the validity of the VaR and TaR forecasts on irregularly spaced data. The performance of the HFR measure is illustrated in an empirical application for two stocks (Bank of America and Microsoft) and an exchange‐traded fund based on Standard & Poor's 500 index. We show that the intraday HFR forecasts capture accurately the volatility and duration dynamics for these three assets. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: High-frequency Risk Measuresmentioning

confidence: 99%

Forecasting High‐Frequency Risk Measures

Banulescu

Colletaz

Hurlin

et al. 2015

Journal of Forecasting

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“…Stange and Kaserer (2008a) analyze the distributional properties of liquidity costs and show that they are heavily skewed and fat tailed. Bangia et al (1999), Giot and Grammig (2005) and Stange and Kaserer (2008c) account for non-normality in the context of risk management.…”

Section: Liquidity Risk Modelmentioning

confidence: 99%

“…The following approach addresses this issue of non-normality, which is also present in other modeling solutions. Giot and Grammig (2005) assume a t-distribution in order to adjust for fat-tails in net returns, i.e. returns net of order-size-adjusted weighted spread.…”

Section: Liquidity Risk Denitionmentioning

confidence: 99%

“…also Zangari (1996), p. 10f. While we applied the Cornish-Fisher approximation to the basic spread model of Bangia et al (1999), analogous use in other liquidity models such as the weighted spread model in Giot and Grammig (2005) or Stange and Kaserer (2008c) is also easily feasible.…”

Section: Modied Liquidity-adjusted Value-at-riskmentioning

confidence: 99%

“…In contrast, the second group makes use of direct liquidity cost measures such as the bid-ask-spread or the order-sizedependent weighted spread. For instance Bangia et al (1999), Francois-Heude and Van Wynendaele (2001), Giot and Grammig (2005) and Stange and Kaserer (2008c) propose models that can be classied into this latter category.…”

Section: Introduction Introductionmentioning

confidence: 99%

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Accounting for nonnormality in liquidity risk

Ernst¹,

Stange²,

Kaserer³

2012

JOR

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It has been frequently discussed, that returns are not normally distributed. Liquidity costs, measuring market liquidity, are similarly non-normally distributed displaying fat tails and skewness. Liquidity risk models either ignore this fact or use the historical distribution to empirically estimate worst losses. We suggest a new and easily implementable, parametric approach based on the Cornish-Fisher approximation to account for non-normality in liquidity risk. We show how to implement this methodology in a large sample of stocks and provide evidence that it produces much more accurate results than an alternative empirical risk estimation.

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The Impact of Liquidity Risk: A Fresh Look*

Stange¹,

Kaserer²

2011

International Review of Finance

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This paper takes a fresh look at the importance of liquidity risk using a comprehensive liquidity measure, weighted spread, in a Value-at-Risk (VaR) framework. The weighted spread measure extracts liquidity costs by order size from the limit order book. Using a unique, representative data set of 160 German stocks over 5.5 years, we show that liquidity risk is an important risk component. Actually, liquidity risk is increasing the total price risk by over 25%, even at 10-day horizons and for liquid blue chip stocks and especially in larger, yet realistic order sizes beyond h1 million. When correcting for liquidity risk, it is commonly assumed that liquidity risk can be simply added to price risk. Our empirical results show that this is not correct, as the correlation between liquidity and price is non-perfect and total risk is thus overestimated.

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How large is liquidity risk in an automated auction market?

Cited by 4 publications

References 27 publications

Forecasting High‐Frequency Risk Measures

Forecasting High‐Frequency Risk Measures

Accounting for nonnormality in liquidity risk

The Impact of Liquidity Risk: A Fresh Look*

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