A simple linear alternative to multiplicative error models with an application to trading volume
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- Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018.
"A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile,"
International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
- Stella Moisan & Rodrigo Herrera & Adam Clements, 2017. "A Dynamic Multiple Equation Approach for Forecasting PM2.5 Pollution in Santiago, Chile," NCER Working Paper Series 117, National Centre for Econometric Research.
- Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011.
"Intra-daily Volume Modeling and Prediction for Algorithmic Trading,"
Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 489-518, Summer.
- Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2009. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Econometrics Working Papers Archive wp2009_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- Engle, Robert F. & Gallo, Giampiero M., 2006.
"A multiple indicators model for volatility using intra-daily data,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
- Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model for Volatility Using Intra-Daily Data," NBER Working Papers 10117, National Bureau of Economic Research, Inc.
- Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Econometrics Working Papers Archive wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- Clements, A.E. & Hurn, A.S. & Li, Z., 2016.
"Forecasting day-ahead electricity load using a multiple equation time series approach,"
European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
- Adam Clements & Stan Hurn & Zili Li, 2014. "Forecasting day-ahead electricity load using a multiple equation time series approach," NCER Working Paper Series 103, National Centre for Econometric Research, revised 06 May 2015.
- Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016.
"Exploiting the errors: A simple approach for improved volatility forecasting,"
Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
- Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2015. "Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting," CREATES Research Papers 2015-14, Department of Economics and Business Economics, Aarhus University.
- Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008.
"Improving VWAP strategies: A dynamic volume approach,"
Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
- Jedrzej Białkowski & Serge Darolles & Gaëlle Le Fol, 2006. "Improving VWAP strategies: A dynamical volume approach," Documents de recherche 06-08, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
- Jędrzej Białkowski & Serge Darolles & Gaëlle Le Fol, 2008. "Improving VWAP strategies: A dynamic volume approach," Post-Print hal-02877984, HAL.
- Jedrzej Bialkowski & Serge Darolles & Gaëlle Le Fol, 2008. "Improving VWAP strategies: A dynamic volume approach," Post-Print halshs-00676946, HAL.
- Naimoli, Antonio & Storti, Giuseppe, 2019.
"Heterogeneous component multiplicative error models for forecasting trading volumes,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
- Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.
- Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Peirson, John & Henley, Andrew, 1994. "Electricity load and temperature : Issues in dynamic specification," Energy Economics, Elsevier, vol. 16(4), pages 235-243, October.
- repec:bla:jfinan:v:43:y:1988:i:1:p:97-112 is not listed on IDEAS
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More about this item
Keywords
Volume; forecasting; high-frequency data; CMEM; diurnal;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G00 - Financial Economics - - General - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CWA-2021-12-20 (Central and Western Asia)
- NEP-ECM-2021-12-20 (Econometrics)
- NEP-ETS-2021-12-20 (Econometric Time Series)
- NEP-FOR-2021-12-20 (Forecasting)
- NEP-ORE-2021-12-20 (Operations Research)
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