Modelling the Dynamics of Cross-Sectional Price Functions: an Econometric Analysis of the Bid and Ask Curves of an Automated Exchange
AbstractFunctional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal function is used to approximate the price curve data. Estimation may then be performed using quasi-maximum likelihood methods based on the Kalman filter. The model is used to provide one of the first studies of the dynamics of the bid and ask curves of an electronic limit order book and enables the comprehensive measurement of the dynamic determinants of traders execution costs. It is found that the differences between the bid and ask curves and their intercepts (i.e. the immediate price impacts of market orders) are well described by covariance stationary processes. The in-sample, 1-step ahead point predictions for these curves perform well and motivate the development of parametric FSN models that take into account the monotonicity of the price curves and can be used to form predictive distributions.
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Bibliographic InfoPaper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2004-W21.
Length: 30 pages
Date of creation: 22 Sep 2004
Date of revision:
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Web page: http://www.nuff.ox.ac.uk/economics/
functional time series; bid and ask curves; liquidity; electronic limit order book; cubic spline; state space form; Kalman filter; quasi-maximum likelihood.;
Other versions of this item:
- Clive G. Bowsher, 2004. "Modelling the Dynamics of Cross-Sectional Price Functions: an Econometric Analysis of the Bid and Ask Curves of an Automated Exchange," OFRC Working Papers Series 2004fe19, Oxford Financial Research Centre.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-12-12 (All new papers)
- NEP-FIN-2004-12-02 (Finance)
- NEP-FMK-2004-12-12 (Financial Markets)
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- Clive Bowsher & Roland Meeks, 2008.
"The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve,"
OFRC Working Papers Series
2008fe24, Oxford Financial Research Centre.
- Bowsher, Clive G. & Meeks, Roland, 2008. "The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1419-1437.
- Clive G. Bowsher & Roland Meeks, 2008. "The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve," Economics Series Working Papers 2008-WO5, University of Oxford, Department of Economics.
- Clive G. Bowsher & Roland Meeks, 2008. "The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve," Economics Papers 2008-W05, Economics Group, Nuffield College, University of Oxford.
- Clive G. Bowsher & Roland Meeks, 2008. "The dynamics of economics functions: modelling and forecasting the yield curve," Working Papers 0804, Federal Reserve Bank of Dallas.
- Holmberg, Ulf, 2012. "Essays on Credit Markets and Banking," UmeÃ¥ Economic Studies 840, Umeå University, Department of Economics.
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