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Realized density estimation using intraday prices Cover
By: Josip Arnerić  
Open Access
|Jun 2020

References

  1. 1. Aït-Sahalia, Y., Mykland, P., Zhang, L. (2005). How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise. Review of Financial Studies, Vol 18, pp. 351-416.10.1093/rfs/hhi016
  2. 2. Aït-Sahalia, Y., Mykland, P., Zhang, L. (2011). Ultra-high frequency volatility estimators with dependent microstructure noise. Journal of Econometrics, Vol. 160, No. 1, pp. 160-175.10.1016/j.jeconom.2010.03.028
  3. 3. Amaya, D., Christoffersen, P., Jacobs, K., Vasquez, A. (2015). Does realized skewness predict the cross-section of equity returns? Journal of Financial Economics, Vol. 118, No. 1, pp. 135-167.10.1016/j.jfineco.2015.02.009
  4. 4. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2001). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, Vol. 96, No. 453, pp. 42-55.10.1198/016214501750332965
  5. 5. Andersen, T. G., Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts. International Economics Review, Vol. 39, No. 4, pp. 885-905.10.2307/2527343
  6. 6. Arnerić, J., Matković, M., Sorić, P. (2019 a). Comparison of range-based volatility estimators against integrated volatility in European emerging markets. Finance Research Letters, Vol. 28, pp. 118-124.10.1016/j.frl.2018.04.013
  7. 7. Arnerić, J., Matković, M. (2019 b). Challenges of integrated variance estimation in emerging stock markets. Journal of Economics and Business: Proceedings of Rijeka Faculty of Economics, Vol. 37, No. 2, pp. 713-739.10.18045/zbefri.2019.2.713
  8. 8. Bandi, F. M., Russell, J. R. (2008). Microstructure Noise, Realized Volatility, and Optimal Sampling. Review of Economic Studies, Vol. 75, No. 2, pp. 339-369.10.1111/j.1467-937X.2008.00474.x
  9. 9. Barndorff-Nielsen, O. E., Shephard, N. (2002). Estimating quadratic variation using realized volatility. Journal of Applied Econometrics, Vol. 17, No. 5, pp. 457-477.10.1002/jae.691
  10. 10. Barndorff-Nielsen, O. E., Shephard, N. (2006). Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation. Journal of Financial Econometrics, Vol. 4, No. 1, pp. 1-30.10.1093/jjfinec/nbi022
  11. 11. Chu, C-Y., Henderson, D., Parmeter, C. (2015). Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data. Econometrics, Vol. 3, No. 2, pp. 199-214.10.3390/econometrics3020199
  12. 12. Grith, M., Härdle, W. K., Schlenle, M. (2012). Nonparametric Estimation of Risk-Neutral Densities. In Handbook of Computational Finace, Duan J. C., Härdle, W. K., Gentle, J. (Eds.), Springer, Berlin, pp. 277-305.10.1007/978-3-642-17254-0_11
  13. 13. Marron, J. S., Nolan, D. (1988). Canonical kernels for density estimation. Statistics & Probability Letters, Vol. 7, No. 3, pp. 195-199.10.1016/0167-7152(88)90050-8
  14. 14. Neuberger, A. (2012). Realized skewness. Review of Financial Studies, Vol. 25, No. 11, pp. 3423-3455.10.1093/rfs/hhs101
  15. 15. Oomen, R. C. A. (2006). Properties of Realized Variance under Alternative Sampling Schemes. Journal of Business & Economic Statistics, Vol. 24, No. 2, pp. 219-237.10.1198/073500106000000044
  16. 16. Park, B. U., Marron J. S. (1992). On the use of pilot estimators in bandwidth selection. Journal of Nonparametric Statistics, Vol. 1, No. 3, pp. 231-240.10.1080/10485259208832524
  17. 17. Parzen, E. (1962). On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics, Vol. 33, No. 3, pp. 1065-1076.10.1214/aoms/1177704472
  18. 18. Racine, J. S. (2008). Nonparametric Econometrics: A Primer. Foundations and Trends in Econometrics, Vol. 3, No. 1, pp. 1-88.10.1561/0800000009
  19. 19. Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics, Vol. 27, No. 3, pp. 832-837.10.1214/aoms/1177728190
  20. 20. Scott, D. W. (2015). Kernel Density Estimators. In Multivariate Density Estimation: Theory, Practice, and Visualization, Scott, D. W. (Ed.), John-Wiley & Sons, Chichester, pp. 137-216.10.1002/9781118575574.ch6
  21. 21. Sheather, S. J., Jones, M. C. (1991). A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation. Journal of the Royal Statistical Society, Vol. 53, No. 3, pp. 683-690.10.1111/j.2517-6161.1991.tb01857.x
  22. 22. Shen, K., Yao, J., Li, W. K. (2018). On the surprising explanatory power of higher realized moments in practice. Statistics and its Interface, Vol. 11. No. 1, pp. 153-168.10.4310/SII.2018.v11.n1.a13
  23. 23. Silverman, B. W. (1986). Density estimation in action. In Density Estimation for Statistics and Data Analysis, Silverman. B. W. (Ed.), Chapman & Hall, New York, pp. 120-158.10.1201/9781315140919-6
  24. 24. Terrel, G. R., Scott, D. V. (1992). Variable Kernel Density Estimation. The Annals of Statistics, Vol. 20, No. 3, pp. 1236-1265.10.1214/aos/1176348768
  25. 25. Zhang, L. (2011). Estimating covariation: Epps Effect, microstructure noise. Journal of Econometrics, Vol. 160, No. 1, pp. 33-47.10.1016/j.jeconom.2010.03.012
  26. 26. Zhang, L., Mykland, P., Aït-Sahalia, Y. (2005). A Tale of Two Time Scales. Journal of American Statistical Association, Vol. 100, No. 472, pp. 1394–1411.10.1198/016214505000000169
  27. 27. Wand, M. P., Jones, M. C. (1995). Kernel Smoothing. Chapman & Hall, New York.10.1007/978-1-4899-4493-1
Language: English
Page range: 1 - 9
Submitted on: Nov 26, 2019
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Accepted on: May 6, 2020
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Published on: Jun 8, 2020
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2020 Josip Arnerić, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.