Bayesian Dynamic Modeling of High-Frequency Integer Price Changes

Arbeitspapier

We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the data via two different approaches: ordered probit models and discrete distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic patterns. We consider distributions with heavy tails to address occurrences of jumps in tick by tick discrete prices changes. In particular, we introduce a dynamic version of the negative binomial difference model with stochastic volatility. For each model we develop a Markov chain Monte Carlo estimation method that takes advantage of auxiliary mixture representations to facilitate the numerical implementation. This new modeling framework is illustrated by means of tick by tick data for several stocks from the NYSE and for different periods. Different models are compared with each other based on predictive likelihoods. We find evidence in favor of our preferred dynamic negative binomial difference model.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 16-028/III

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Financial Econometrics
• Subject: Bayesian inference ; discrete distributions ; high-frequency dynamics ; Markov chain Monte Carlo ; stochastic volatility

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Barra, Istvan ; Koopman, Siem Jan
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/145335
• Last update: 10.03.2025, 11:42 AM CET