Ordinal-response models for irregularly spaced transactions: A forecasting exercise

Stefanos Dimitrakopoulos, Mike Tsionas and Abdelhakim Aknouche

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a new model for transaction data that accounts jointly for the time duration between transactions and for the discreteness of the intraday stock price changes. Duration is assumed to follow a stochastic conditional duration model, while price discreteness is captured by an autoregressive moving average ordinal-response model with stochastic volatility and time-varying parameters. The proposed model also allows for endogeneity of the trade durations as well as for leverage and in-mean effects. In a purely Bayesian framework we conduct a forecasting exercise using multiple high-frequency transaction data sets and show that the proposed model produces better point and density forecasts than competing models.

Keywords: Ordinal-response models; irregularly spaced data; stochastic conditional duration; time varying ARMA-SV model; Bayesian MCMC; model confidence set. (search for similar items in EconPapers)
JEL-codes: C1 C11 C15 C4 C41 C5 C51 C53 C58 (search for similar items in EconPapers)
Date: 2020-10-01, Revised 2020-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:103250

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