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Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities

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  • Nikolaus Hautsch

Abstract

In this article we introduce the concept of excess volume durations, which are defined as the time until a given amount of buy or sell excess volume is traded on the market. Excess volume durations indicate the one-sided intensity of liquidity demand and characterize the risk of a market maker with respect to asymmetric information and inventory problems. By modeling excess volume durations based on Box--Cox-type autoregressive conditional duration (ACD) models, it is shown that market microstructure variables are predictors for the expected liquidity demand intensity. Moreover, the length of excess volume durations is found to be positively correlated with the magnitude of the corresponding price impact and thus the market depth. , .

Suggested Citation

  • Nikolaus Hautsch, 2003. "Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 189-215.
    • Handle: RePEc:oup:jfinec:v:1:y:2003:i:2:p:189-215
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    Citations

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    Cited by:

    1. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    2. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    3. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    4. Renault, Eric & Werker, Bas J.M., 2011. "Causality effects in return volatility measures with random times," Journal of Econometrics, Elsevier, vol. 160(1), pages 272-279, January.
    5. Aneta Hryckiewicz & Piotr Mielus & Karolina Skorulska & Malgorzata Snarska, 2018. "Does a bank levy increase frictions on the interbank market?," KAE Working Papers 2018-033, Warsaw School of Economics, Collegium of Economic Analysis.
    6. Petra Tomanová & Vladimír Holý, 2021. "Clustering of arrivals in queueing systems: autoregressive conditional duration approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 859-874, September.
    7. Francisco Blasques & Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Papers 1812.07318, arXiv.org, revised Jan 2022.
    8. Nikolaus Hautsch & Vahidin Jeleskovic, 2008. "Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models," SFB 649 Discussion Papers SFB649DP2008-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 89-121.
    10. repec:wyi:journl:002120 is not listed on IDEAS
    11. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

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