The influence of covariates on absolute log returns of ultra high frequency data is analysed. Therefore we construct a mixed effect model for the absolute log returns. The parameters are estimated in a state space approach. To analyse the correlation in these irregularly spaced data empirically, the variogram, known mainly from spatial statistics, will be used. In a small simulation study the performance of the estimators will be analysed. In the end we apply the model to IBM trade data and analyse the influence of the covariates

Czado, Claudia

Haug, Stephan

English

Universität München: Elektronischen Publikationen

Mixed effect model for absolute log returns of ultra high frequency data

Open Access LMU

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A Discrete-State Continuous-Time Model of Financial Transactions Prices and Times: The Autoregressive Conditional MultinomialAutoregressive Conditional Duration Model.

A Dynamic Integer Count Data Model for Financial Transaction Prices.

A Note on Non-negative Continuous-time Processes.

A Simple Long Memory Model of Realized Volatility.

Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica

Econometric analysis of realized volatility and its use in estimating stochastic volatility models.

Econometric Modelling of Stock Market Intraday Activity.

Estimation in generalized linear models with random eﬀects.

Forecasting strucural time series models and the Kalman ﬁlter.

Generalized Additive Models.

Generalized Linear Models (2nd edn).

How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise. Review of Financial Studies

Inference and Speciﬁcation Analysis.

L` evy-driven and fractionally integrated ARMA processes with continuous time parameter. Statistica Sinica

L´ evy processes and stochastic calculus.

Longitudinal Data with Serial Correlation: A State-space Approach.

Model-based measurement of actual volatility in high-frequency data. Advances in Econometrics;

Modelling High-Frequency Data in Continuous Time.

Non-Gaussian Ornstein- Uhlenbeck based models and some of their uses in ﬁnancial economics (with discussion).

On the sample variogram and the sample autocovariance for nonstationary time series. The Statistician

Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility.

Stochastic volatility models with transaction time risk. Discussion paper 24

Strictly Proper Scoring Rules, Prediction, and Estimation.

The econometrics of ultra high frequency data. Econometrica

The MIDAS touch: Mixed Data Sampling Regressions.

Time Series Analysis by State Space Methods.

Why do absolute returns predict volatility so well?.

this paper given the past information  G  t i-1 = #(S t j , d t j ; j      1) and current duration d t i = t i    t i-1 . Since the duration process is a stochastic process itself one also needs a model for this regularly spaced (measured in tick time) time series. A popular model for the durations given the past information, called Autoregressive Conditional Duration (ACD) model, has been proposed by Engle and Russell (1997). There are a number of modifications of the ACD model, which are described for example in Bauwens et al. (2004

Stephan Haug

Claudia Czado

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https://epub.ub.uni-muenchen.de/1809/1/paper_440.pdf

Mixed effect model for absolute log returns of ultra high frequency data

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