Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

1. Adhikari, R., Agrawal, R. K. (2013), “An Introductory Study on Time Series Modeling and Forecasting”, LAP Lambert Academic Publishing, Germany.Search in Google Scholar

2. Aït-Sahalia, Y., Mykland, P. A., Zhang, L. (2005), “How often to sample a continuous-time process in the presence of market microstructure noise”, Review of Financial Studies, Vol. 18, No. 2, pp. 351-416.10.1093/rfs/hhi016Search in Google Scholar

3. Aljinović, Z., Poklepović, T. (2013), “Neural networks and vector autoregressive model in forecasting yield curve”, The 6th International Conference on Information Technology (ICIT), Al-Zaytoonah University of Jordan, Amman, Vol. 1, pp. 1-8.Search in Google Scholar

4. Al-Maqaleh, B. M., Al-Mansoub, A. A., Al-Badani, F. N. (2016), “Forecasting using Artificial Neural Network and Statistics Models”, International Journal of Education and Management Engineering, Vol. 6, No. 3, pp. 20-32.10.5815/ijeme.2016.03.03Search in Google Scholar

5. Aminian, F., Suarez, E. D., Aminian, M., Walz, D. T. (2006), “Forecasting Economic Data with Neural Networks”, Computational Economics, Vol. 28, No. 1, pp. 71-88.10.1007/s10614-006-9041-7Search in Google Scholar

6. Andersen, T. G., Bollerslev, T., Diebold, F. X. (2007a), “Roughing it up: including jump components in the measurement, modelling and forecasting of return volatility”, The Review of Economics and Statistics, Vol. 89, No. 4, pp. 701-720.10.1162/rest.89.4.701Search in Google Scholar

7. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2003), “Modeling and Forecasting Realized Volatility”, Econometrica, Vol. 71, No. 2, pp. 579-625.10.1111/1468-0262.00418Search in Google Scholar

8. Andersen, T., Bollerslev, T., Dobrev, D. (2007b), “No-arbitrage semi-martingale restrictions for continuous time volatility models subject to leverage effects, jumps and i.i.d. noise: theory and testable distributional implications”, Journal of Econometrics, Vol. 138, No. 1, pp.125-180.10.1016/j.jeconom.2006.05.018Search in Google Scholar

9. Angus, J. E. (1991), “Criteria For Choosing The Best Neural Network: Part I”, Report No. 91-16.Search in Google Scholar

10. Arnerić, J., Poklepović, T. (2016), “Nonlinear Extension of Asymmetric GARCH Model within Neural Network Framework”, in Zizka, J., Nagamalai, D. (eds.). Computer Science & Information Technology, AIRCC Publishing Corporation, Chennai, India, pp. 101-111.10.5121/csit.2016.60609Search in Google Scholar

11. Arnerić, J., Poklepović, T., Aljinović, Z. (2014), “GARCH based artificial neural networks in forecasting conditional variance of stock returns”, Croatian Operational Research Review, Vol. 5, No. 2, pp. 329-343.10.17535/crorr.2014.0017Search in Google Scholar

12. Bandi, F. M., Russell, J. R. (2008), “Microstructure Noise, Realized Variance, and Optimal Sampling”, The Review of Economic Studies, Vol. 75, No. 1, pp. 339-369.10.1111/j.1467-937X.2008.00474.xSearch in Google Scholar

13. Bandi, F. M., Russell, J. R. (2011), “Market Microstructure Noise, Integrated Variance Estimators, and the Accuracy of Asymptotic Approximations”, Journal of Econometrics, Vol. 160, No. 1, pp. 145-159.10.1016/j.jeconom.2010.03.027Search in Google Scholar

14. Barndorff-Nielsen, O. E., Shephard, N. (2002a), “Econometric Analysis of Realised Volatility and its use in Estimating Stochastic Volatility Models”, Journal of the Royal Statistical Society, Vol. 64, No. 2, pp. 253-280.10.1111/1467-9868.00336Search in Google Scholar

15. Barndorff-Nielsen, O. E., Shephard, N. (2002b), “Estimating Quadratic Variation Using Realized Variance”, Journal of Applied Econometrics, Vol. 17, No. 5, pp. 457-478.10.1002/jae.691Search in Google Scholar

16. Barndorff-Nielsen, O. E., Shephard, N. (2004), “Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics”, Econometrica, Vol. 72, No. 3, pp. 885-925.10.1111/j.1468-0262.2004.00515.xSearch in Google Scholar

17. Baruník, J., Křehlík, T. (2016), “Combining high frequency data with non-linear models for forecasting energy market volatility”, Expert Systems With Applications, Vol. 55, pp. 222-242.10.1016/j.eswa.2016.02.008Search in Google Scholar

18. Bektipratiwi, A., Irawan, M. I. (2011), “A RBF-EGARCH neural network model for time series forecasting”, Proceedings of The IceMATH 2011, pp. 1-8.Search in Google Scholar

19. Bildirici, M., Ersin, Ö. Ö. (2009), “Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange”, Expert Systems with Applications, Vol. 36, No. 4, pp. 7355-7362.10.1016/j.eswa.2008.09.051Search in Google Scholar

20. Bildirici, M., Ersin, Ö. Ö. (2012), “Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models”, MPRA Paper No. 40330.Search in Google Scholar

21. Bildirici, M., Ersin, Ö. Ö. (2014), “Modelling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns”, Hindawi Publishing Corporation: The Scientific World Journal, Article ID 497941, pp. 1-21.Search in Google Scholar

22. Binner, J. M., Elger, C. T., Nilsson, B., Tepper, J. A. (2006), “Predictable non-linearities in U.S. inflation”, Economics Letters, Vol. 93, No. 3, pp. 323-328.10.1016/j.econlet.2006.06.001Search in Google Scholar

23. Bollerslev, T. (1986), “Generalized autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, Vol. 31, No. 3, pp. 307-327.10.1016/0304-4076(86)90063-1Search in Google Scholar

24. Buse, A. (1982), “The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note”, The American Statistician, Vol. 36, No. 3, pp. 153-157.10.2307/2683166Search in Google Scholar

25. Chaudhuri, T. D, Ghosh, I. (2016), „Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework“, Journal of Insurance and Financial Management, Vol. 1, No. 5, pp. 92-123.Search in Google Scholar

26. Choudhary, A., Haider, A. (2008), “Neural network models for inflation forecasting: an appraisal”, Discussion Papers in Economics, University of Surrey, UK, DP 08/08.Search in Google Scholar

27. Clements, A. E., Liao, Y. (2013), “Modeling and forecasting realized volatility: getting the most out of the jump component”, NCER Working Paper Series, Working Paper #93, August.Search in Google Scholar

28. Corsi, F. (2003), “A Simple Long Memory Model of Realized Volatility”, manuscript, University of Southern Switzerland.10.2139/ssrn.626064Search in Google Scholar

29. Corsi, F. (2009), “A simple approximate long memory model of realized volatility”, Journal of Financial Econometrics, Vol. 7, No. 2, pp. 174-196.10.1093/jjfinec/nbp001Search in Google Scholar

30. Corsi, F., Audrino, F., Reno, R. (2012), “HAR Modeling for Realized Volatility Forecasting”, in Handbook of Volatility Models and Their Applications, John Wiley & Sons, New Jersey, USA, pp. 363-382.10.1002/9781118272039.ch15Search in Google Scholar

31. Dedi, R., Yoga, A. N., Rahmawati, K. D. (2011), ”Forecasting the Indonesian Government Securities Yield Curve Using Neural Networks and Vector Autoregressive Model“, ISI 58th Session, Dublin, August 21-26, 2011.Search in Google Scholar

32. Degiannakis, S., Floros, C. (2015), “Modelling and Forecasting High Frequency Financial Data”, Palgrave Macmillan.10.1057/9781137396495Search in Google Scholar

33. Diebold, F. X., Mariano, R. S. (1995), “Comparing Predictive Accuracy”, Journal of Business and Economic Statistics, Vol. 13, No. 3, pp. 253-263.Search in Google Scholar

34. Doukim, C. A., Dargham, J. A., Chekima, A. (2010), “Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique”, in Proceedings of the 10th International Conference on Information Sciences, Signal Processing and Their Applications (ISSPA '10), May, pp. 606-609.10.1109/ISSPA.2010.5605430Search in Google Scholar

35. Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, Vol. 50, No. 4, pp. 987-1007.10.2307/1912773Search in Google Scholar

36. Franses, P. H., van Dijk, D. (2003), “Nonlinear Time Series Models in Empirical Finance”, Cambridge University Press.Search in Google Scholar

37. Gonzales, S. (2000), “Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models”, Working Paper 2000-07.Search in Google Scholar

38. Hagiwara, M. (1994), “A simple and effective method for removal of hidden units and weights”, Neurocomputing, Vol. 6, No. 2, pp. 207-218.10.1016/0925-2312(94)90055-8Search in Google Scholar

39. Hansen, P. R., Lunde, A. (2005), “A realized variance for the whole day based on intermittent high-frequency data”, Journal of Financial Econometrics, Vol. 3, No. 4, pp. 525-554.10.1093/jjfinec/nbi028Search in Google Scholar

40. Hansen, P. R., Lunde, A. (2006), “Realized variance and market microstructure noise”, Journal of Business and Economic Statistics, Vol. 24, No. 2, pp. 127-161.10.1198/073500106000000071Search in Google Scholar

41. Huang, C., Gong, X., Chen, X., Wen, F. (2013), “Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model”, Research Article, Hindawi Publishing Corporation Abstract and Applied Analysis, Article ID 143194, pp. 1-13.10.1155/2013/143194Search in Google Scholar

42. Junior, M. V. W., Pereira, P. L. V. (2011), “Modeling and Forecasting Realized Volatility: Evidence from Brazil”, Brazilian Review of Econometrics, Vol. 31, No. 2, pp. 315-33710.12660/bre.v31n22011.4056Search in Google Scholar

43. Jurkovič, J. (2013), “Forecasting Realized Volatility Using Neural Networks”, Master thesis, Charles University in Prague Faculty of Social SciencesSearch in Google Scholar

44. Kaashoek, J. F., van Dijk, H. K. (2001), “Neural networks as econometric tool”, Econometric Institute Report, EI 2001-05, pp. 1-32.Search in Google Scholar

45. Keeni, K., Nakayama, K., Shimodaira, H. (1999), “Estimation of initial weights and hidden units for fast learning of multi-layer neural networks for pattern classification”, in Proceedings of the International Joint Conference on Neural Networks (IJCNN '99), Vol. 3, IEEE, July, pp. 1652-1656.10.1109/IJCNN.1999.832621Search in Google Scholar

46. Li, J. Y., Chow, T. W. S., Yu, Y. L. (1995), “Estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network”, in Proceedings of the IEEE International Conference on Neural Networks, Vol. 3, December, pp. 1229-1233.Search in Google Scholar

47. Mantri, J. K., Gahan, P., Nayak, B. B. (2010), “Artificial Neural Networks - An Application to Stock Market Volatility”, International Journal of Engineering Science and Technology, Vol. 2, No. 5, pp. 1451-1460.Search in Google Scholar

48. Mantri, J. K., Mohanty, D., Nayak, B.B. (2012), “Design Neural Network for Stock Market Volatility: Accuracy Measurement”, International Journal on Computer Technology and Applications, Vol. 3, No. 1, pp. 242-250.Search in Google Scholar

49. Medeiros, M. C., Teräsvirta, T., Rech, G. (2006), “Building Neural Network Models for Time Series: A Statistical Approach”, Journal od Forecasting, No. 25, No. 1, pp. 49-75.10.1002/for.974Search in Google Scholar

50. Moshiri, S., Cameron, N. (2000), “Neural Networks Versus Econometric Models in Forecasting Inflation”, Journal of Forecasting, Vol. 19, No. 3, pp. 201-217.10.1002/(SICI)1099-131X(200004)19:3<201::AID-FOR753>3.0.CO;2-4Search in Google Scholar

51. Rosa, R., Maciel, L., Gomide, F., Ballini, R. (2014), “Evolving Hybrid Neural Fuzzy Network for Realized Volatility Forecasting with Jumps”, IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), London, pp. 481-488.10.1109/CIFEr.2014.6924112Search in Google Scholar

52. Teräsvirta, T., Tjøstheim, D., Granger, C. W. J. (2008), “Modelling nonlinear economic time series”, Oxford University Press, New York,.Search in Google Scholar

53. Teräsvirta, T., van Dijk, D., Medeiros, M. C. (2005), “Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A reexamination”, International Journal of Forecasting, Vol. 21, No. 4, pp. 755-774.10.1016/j.ijforecast.2005.04.010Search in Google Scholar

54. Wang, J., Wang, J., Fang, W., Niu, H. (2016), “Financial Time Series Prediction Using Elman Recurrent Random Neural Networks“, Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Article ID 4742515, pp. 1-14.10.1155/2016/4742515Search in Google Scholar

55. Zapranis, A., Refenes, A.-P. (1999), “Neural model identification, variable selection and model adequacy”, Journal of Forecasting, Vol. 18, No. 5, pp. 299-332.10.1002/(SICI)1099-131X(199909)18:5<299::AID-FOR725>3.0.CO;2-TSearch in Google Scholar

56. Zekić-Sušac, M., Kliček, B. (2002), “A Nonlinear Strategy of Selecting NN Architectures for Stock Return Predictions”, Finance, Proceedings from the 50th Anniversary Financial Conference Svishtov, Bulgaria, 11-12 April, Svishtov, Veliko Tarnovo, ABAGAR, pp. 325-355.Search in Google Scholar

57. Zhang, G. P. (2003), “Time series forecasting using hybrid ARIMA and neural network model”, Neurocomputing, Vol. 50, pp. 159-175.10.1016/S0925-2312(01)00702-0Search in Google Scholar

58. Zhang, J., Morris, A. J. (1998), “A sequential learning approach for single hidden layer neural networks”, Neural Networks, Vol. 11, No. 1, pp. 65-80.10.1016/S0893-6080(97)00111-1Search in Google Scholar

59. Zhang, L., Aït-Sahalia, Y., Mykland, P. A. (2005), “A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data”, Journal of the American Statistical Association, Vol. 100, No. 472, pp. 1394-1411.10.1198/016214505000000169Search in Google Scholar