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Modelling Intraday Trading Activity Using Box-Cox-ACD Models

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  • Hautsch, Nikolaus

Abstract

In this paper, I model the intraday trading activity based on volume durations, i.e. the waiting time until a predetermined volume is absorbed by the market. Since this concept measures the trading volume per time it is strongly related to market liquidity. I focus on volumes measured independently of the side of the market as well as on buy volumes, sell volumes and volumes measured on both market sides simultaneously. For econometric modelling of the different duration concepts, the performance of alternative types of Box-Cox-ACD models are analyzed. By evaluating out-of-sample forecasts, evidence is provided that Box-Cox-ACD models are a valuable tool for predicting volume durations. It is shown that volume durations measured independently of the side of the market have the best predictability. Furthermore, I illustrate that the inclusion of explanatory variables capturing past market activities concerning the price process and imbalances between the buy and sell side of the market. The empirical study uses IBM transaction data from the NYSE.

Suggested Citation

  • Hautsch, Nikolaus, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Papers 02/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:0205
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    Citations

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    Cited by:

    1. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    2. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.
    3. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    4. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    5. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    6. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.

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    More about this item

    Keywords

    volume durations; liquidity concepts; Generalized F distribution; out-of-sample-forecasts;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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