Arbeitspapier
Bayesian Dynamic Modeling of High-Frequency Integer Price Changes
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 16-028/III
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Financial Econometrics
- Thema
-
Bayesian inference
discrete distributions
high-frequency dynamics
Markov chain Monte Carlo
stochastic volatility
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Barra, Istvan
Koopman, Siem Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2016
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Barra, Istvan
- Koopman, Siem Jan
- Tinbergen Institute
Entstanden
- 2016
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