A family of autoregressive conditional duration models

Fernandes, Marcelo; Grammig, Joachim

doi:10.1016/j.jeconom.2004.08.016

Cited by 144 publications

(102 citation statements)

References 17 publications

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“…Alternative ways to evaluate MEMs are Lagrange Multiplier tests as proposed by Meitz and Teräsvirta (2006), (integrated) conditional moment tests as discussed by Hautsch (2006) or nonparametric tests as suggested by Fernandes and Grammig (2006).…”

Section: The Vector Memmentioning

confidence: 99%

Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

Hautsch

Jeleskovic²

2008

SSRN Journal

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In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as trading costs in an electronic limit order book market. Using data from the Australian Stock Exchange we model 1-min squared mid-quote returns, average trade sizes, number of trades and average (excess) trading costs per time interval in terms of a four-dimensional multiplicative error model. The latter is augmented to account also for zero observations. We find evidence for significant contemporaneous relationships and dynamic interdependencies between the individual variables. Liquidity is causal for future volatility but not vice versa. Furthermore, trade sizes are negatively driven by past trading intensities and trading costs. Finally, excess trading costs mainly depend on their own history.

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Section: The Vector Memmentioning

confidence: 99%

Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

Hautsch

Jeleskovic²

2008

SSRN Journal

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“…If the data is in the form of a single long string of event recurrence times, it might be of interest to predict the next event recurrence time by exploiting potential dependence of the waiting times between events on past events or on exogenous covariates. Models of this type include the self-exciting point process (Hawkes, 1971;Ogata, 1978), the modulated renewal process (Cox, 1972;Oakes & Cui, 1994;Lin & Fine, 2009) and the autoregressive conditional duration models (Engle & Russell, 1998;Fernandes & Grammig, 2006). Another form of data, which appears most often in medical statistics, consists of multiple strings of event times and covariates for each string.…”

Section: Introductionmentioning

confidence: 99%

Modeling Event Clustering Using the m-Memory Cox-Type Self-Exciting Intensity Model

Chen¹,

Chen²

2014

IJSP

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In the analysis of point processes or recurrent events, the self-exciting component can be an important factor in understanding and predicting event occurrence. A Cox-type self-exciting intensity point process is generally not a proper model because of its explosion in finite time. However, the model with m-memory is appropriate to analyze sequences of recurrent events. It assumes the most recent m events multiplicatively affect the conditional intensity of event occurrence. Aside from the interpretability, one advantage is the simplicity of the estimation and inference-the Cox partial likelihood can be applied and the resulting estimator is consistent and asymptotically normal. Another advantage is that the model can be applied to the analysis of case-cohort data via the pseudolikelihood approach. The simulation studies support the asymptotic theory. Application is illustrated with analysis of a bladder cancer dataset and of an Australian stock index dataset, which shows evidence of self-excitation.

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“…Higgins andBera, 1992, andGoncalves andMeddahi, 2011) and price durations (Fernandes and Grammig, 2006). This paper contributes to the debate in two ways: first, we propose a fast nonparametric method based on the estimation of the prediction error variance (p.e.v.)…”

Section: Introductionmentioning

confidence: 99%

Does the Box–Cox transformation help in forecasting macroeconomic time series?

Proietti

Lütkepohl

2013

International Journal of Forecasting

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The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the BoxCox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-stepahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the naive predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance. Department of Economics European University Institute AbstractThe paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-step-ahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the naïve predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance.

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A family of autoregressive conditional duration models

Cited by 144 publications

References 17 publications

Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models

Modeling Event Clustering Using the m-Memory Cox-Type Self-Exciting Intensity Model

Does the Box–Cox transformation help in forecasting macroeconomic time series?

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