Regression Models for Ordinal Valued Time Series with Application to High Frequency Financial Data

Müller, Gernot und Czado, Claudia (2002): Regression Models for Ordinal Valued Time Series with Application to High Frequency Financial Data. Sonderforschungsbereich 386, Discussion Paper 301

Abstract

In financial time series transaction price changes often occur in discrete increments, for example in eights of a dollar. We consider these price changes as discrete random variables which are assumed to be generated by a latent process which incorporates both exogenous variables and autoregressive components. A standard Gibbs sampling algorithm has been developed to estimate the parameters of the model. However this algorithm exhibits bad convergence properties. To improve the standard Gibbs sampler we utilize methods proposed by Liu and Sabatti (2000, Biometrika 87), based on transformation groups on the sample space. A simulation study will be given to demonstrate the substantial improvement by this new algorithm. Finally we apply our model to the data of the IBM stock on Nov 13, 2000, and estimate the influence of the duration between transactions, the volume, and the bid-offer-spread both to model fit and prediction.

• Dokumententyp: Paper
• Keywords: Bayesian inference; Discrete-valued time series; High-frequency finance; Markov Chain Monte Carlo; Multigrid Monte Carlo
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Sonderforschungsbereich 386 ; Sonderforschungsbereiche > Sonderforschungsbereich 386
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch