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Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

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  • Nikolaus Hautsch
  • Vahidin Jeleskovic

Abstract

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as trading costs in an electronic limit order book market. Using data from the Australian Stock Exchange we model 1-min squared mid-quote returns, average trade sizes, number of trades and average (excess) trading costs per time interval in terms of a four-dimensional multiplicative error model. The latter is augmented to account also for zero observations. We find evidence for significant contemporaneous relationships and dynamic interdependencies between the individual variables. Liquidity is causal for future volatility but not vice versa. Furthermore, trade sizes are negatively driven by past trading intensities and trading costs. Finally, excess trading costs mainly depend on their own history.

Suggested Citation

  • 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.
    • Handle: RePEc:hum:wpaper:sfb649dp2008-047
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    References listed on IDEAS

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    Cited by:

    1. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Będowska-Sójka, Barbara & Kliber, Agata, 2019. "The causality between liquidity and volatility in the Polish stock market," Finance Research Letters, Elsevier, vol. 30(C), pages 110-115.
    3. Anastasios Demertzidis & Vahidin Jeleskovic, 2021. "Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID," JRFM, MDPI, vol. 14(5), pages 1-23, May.
    4. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    5. Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Szymon Borak & Rafał Weron, 2008. "A semiparametric factor model for electricity forward curve dynamics," SFB 649 Discussion Papers SFB649DP2008-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

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    More about this item

    Keywords

    Multiplicative error models; volatility; liquidity; high-frequency data;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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