IDEAS home Printed from https://ideas.repec.org/r/fir/econom/wp2009_01.html
   My bibliography  Save this item

Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Torben G. Andersen & Oleg Bondarenko & Albert S. Kyle & Anna Obizhaeva, 2016. "Intraday Trading Invariance in the E-mini S&P 500 Futures Market," Working Papers w0229, New Economic School (NES).
  2. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
  3. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
  4. Szűcs, Balázs Árpád, 2017. "Forecasting intraday volume: Comparison of two early models," Finance Research Letters, Elsevier, vol. 21(C), pages 249-258.
  5. 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.
  6. Shaojun Ma & Pengcheng Li, 2021. "Predicting Daily Trading Volume via Various Hidden States," Papers 2107.07678, arXiv.org.
  7. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
  8. Dutt, Tanuj & Humphery-Jenner, Mark, 2013. "Stock return volatility, operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 999-1017.
  9. Lei Li & Zhiyuan Zhang & Ruihan Bao & Keiko Harimoto & Xu Sun, 2022. "Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction," Papers 2208.07232, arXiv.org.
  10. Giampiero M. Gallo & Edoardo Otranto, 2018. "Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 549-573, April.
  11. Axel Groß‐KlußMann & Nikolaus Hautsch, 2013. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(8), pages 724-742, December.
  12. Clements, Adam & Hurn, Stan & Volkov, Vladimir, 2021. "A simple linear alternative to multiplicative error models with an application to trading volume," Working Papers 2021-06, University of Tasmania, Tasmanian School of Business and Economics.
  13. Christian Conrad & Robert F. Engle, 2021. "Modelling Volatility Cycles: The (MF)2 GARCH Model," Working Paper series 21-05, Rimini Centre for Economic Analysis.
  14. Francesco Calvori & Fabrizio Cipollini & Giampiero M. Gallo, 2014. "Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares," Econometrics Working Papers Archive 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
  15. Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024. "Doubly multiplicative error models with long- and short-run components," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
  16. Taras Bodnar & Nikolaus Hautsch, 2012. "Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes," SFB 649 Discussion Papers SFB649DP2012-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  17. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
  18. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
  19. Demetrio Lacava & Giampiero M. Gallo & Edoardo Otranto, 2022. "Unconventional policies effects on stock market volatility: The MAP approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1245-1265, November.
  20. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
  21. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
  22. Wolfgang K. Härdle & Nikolaus Hautsch & Andrija Mihoci, 2015. "Local Adaptive Multiplicative Error Models for High‐Frequency Forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 529-550, June.
  23. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
  24. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 89-121.
  25. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723.
  26. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
  27. G.M. Gallo & D. Lacava & E. Otranto, 2020. "Measuring the Effects of Unconventional Policies on Stock Market Volatility," Working Paper CRENoS 202006, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  28. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
  29. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378, April.
  30. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
  31. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
  32. 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.
  33. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
  34. Vladimir Markov & Olga Vilenskaia & Vlad Rashkovich, 2019. "Quintet Volume Projection," Papers 1904.01412, arXiv.org.
  35. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
  36. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.