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Multiplicative Error Models

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Abstract

Financial time series analysis has focused on data related to market trading activity. Next to the modeling of the conditional variance of returns within the GARCH family of models, recent attention has been devoted to other variables: first, and foremost, volatility measured on the basis of ultra-high frequency data, but also volumes, number of trades, durations. In this paper, we examine a class of models, named Multiplicative Error Models, which are particularly suited to model such non-negative time series. We discuss the univariate specification, by considering the base choices for the conditional expectation and the error term. We provide also a general framework, allowing for richer specifications of the conditional mean. The outcome is a novel MEM (called Composite MEM) which is reminiscent of the short- and long-run component GARCH model by Engle and Lee (1999). Inference issues are discussed relative to Maximum Likelihood and Generalized Method of Moments estimation. In the application, we show the regularity in parameter estimates and forecasting performance obtainable by applying the MEM to the realized kernel volatility of components of the S&P100 index. We suggest extensions of the base model by enlarging the information set and adopting a multivariate specification.

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Bibliographic Info

Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number 2011_03.

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Length: 26 pages
Date of creation: Feb 2011
Date of revision: Apr 2011
Publication status: Forthcoming in 'Volatility Models and Their Applications' (Luc Bauwens, Christian Hafner, Sebastien Laurent eds.)
Handle: RePEc:fir:econom:wp2011_03

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Keywords: Multiplicative Error Models; Realized Volatility; Financial Time Series; Composite MEM;

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References

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  1. Giovanni De Luca & Giampiero Gallo, 2010. "A Time-varying Mixing Multiplicative Error Model for Realized Volatility Abstract: In this paper we model the dynamics of realized volatility as a Multiplicative Error Model with a mixture of distribu," Econometrics Working Papers Archive wp2010_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
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Cited by:
  1. Cosma, Antonio & Galli, Fausto, 2014. "A non parametric ACD model," MPRA Paper 53990, University Library of Munich, Germany.
  2. Francq, Christian & Sucarrat, Genaro, 2013. "An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation," MPRA Paper 51783, University Library of Munich, Germany.
  3. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
  4. Genaro Sucarrat & Álvaro Escribano, 2013. "Unbiased QML Estimation of Log-GARCH Models in the Presence of Zero Returns," Economics Working Papers we1321, Universidad Carlos III, Departamento de Economía.
  5. Liu, Xiaochun, 2011. "Modeling the time-varying skewness via decomposition for out-of-sample forecast," MPRA Paper 41248, University Library of Munich, Germany.
  6. E. Otranto, 2012. "Spillover Effects in the Volatility of Financial Markets," Working Paper CRENoS 201217, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  7. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," CORE Discussion Papers 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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