A Markov Chain Estimator of Multivariate Volatility from High Frequency Data

Hansen, Peter Reinhard; Horel, Guillaume; Lunde, Asger; Archakov, Ilya

doi:10.1007/978-3-319-25826-3_17

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some exceptions are Smith and Khaled (2012), who estimate discrete copulas via a Bayesian data augmentation method, and Heinen and Rengifo (2007), who use a multivariate analysis based on a vector autoregressive conditional Poisson model and a continuous modification of the discrete variable treatment of Denuit and Lambert (2005). Statistical treatments of discrete-valued price changes have been presented in Barndorff-Nielsen et al (2012), Hansen, Horel, Lunde, and Archakov (2016), Koopman, Lit, and Lucas (2017), and Shephard and Yang (2017).…”

Section: Introductionmentioning

confidence: 99%

Dynamic discrete copula models for high‐frequency stock price changes

Koopman

Lit

Opschoor

2018

J of Applied Econometrics

View full text Add to dashboard Cite

We develop a dynamic model for the intraday dependence between discrete stock price changes. The conditional copula mass function for the integer tick-size price changes has time-varying parameters that are driven by the score of the predictive likelihood function. The marginal distributions are Skellam and also have score-driven time-varying parameters. We show that the integration steps in the copula mass function for large dimensions can be accurately approximated via numerical integration. The resulting computational gains lead to a methodology that can treat high-dimensional applications. Its accuracy is shown by an extensive simulation study. In our empirical application of 10 US bank stocks, we reveal strong evidence of time-varying intraday dependence patterns: Dependence starts at a low level but generally rises during the day. Based on one-step-ahead out-of-sample density forecasting, we find that our new model outperforms benchmarks for intraday dependence such as the cubic spline model, the fixed correlation model, or the rolling average realized correlation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic discrete copula models for high‐frequency stock price changes

Koopman

Lit

Opschoor

2018

J of Applied Econometrics

View full text Add to dashboard Cite

show abstract

New evidence on market response to public announcements in the presence of microstructure noise

Bian

Serra

Garcia

et al. 2022

European Journal of Operational Research

View full text Add to dashboard Cite

Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model

Koopman

Lit

Lucas

2017

Journal of the American Statistical Association

View full text Add to dashboard Cite

We study intraday stochastic volatility for four liquid stocks traded on the New York Stock Exchange using a new dynamic Skellam model for high-frequency tick-by-tick discrete price changes. Since the likelihood function is analytically intractable, we rely on numerical methods for its evaluation. Given the high number of observations per series per day (1000 to 10,000), we adopt computationally efficient methods including Monte Carlo integration. The intraday dynamics of volatility and the high number of trades without price impact require nontrivial adjustments to the basic dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive dayto-day forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches. Supplementary materials for this article are available online.

show abstract

A Markov Chain Estimator of Multivariate Volatility from High Frequency Data

Cited by 3 publications

References 35 publications

Dynamic discrete copula models for high‐frequency stock price changes

Dynamic discrete copula models for high‐frequency stock price changes

New evidence on market response to public announcements in the presence of microstructure noise

Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model

Contact Info

Product

Resources

About