LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: John Wiley & Sons, Inc
Languages: English
Types: Part of book or chapter of book
Subjects: HB
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Y. A¨ıt-Sahalia and J. Jacod. Testing for jumps in a discretely observed process. Annals of Statistics, 37:184-222, 2009.
    • 2. Y. A¨ıt-Sahalia and L. Mancini. Out of sample forecasts of quadratic variation. Journal of Econometrics, 147(1):17-33, 2008.
    • 3. T. Andersen, T. Bollerslev, and F. X. Diebold. Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics, 89:701-720, 2007.
    • 4. A. Arneodo, J.F. Muzy, and D. Sornette. Casual cascade in stock market from the "infrared" to the "ultraviolet". European Physical Journal B, 2:277, 1998.
    • 5. F. Audrino and P. Bu¨hlmann. Tree-structured garch models. Journal of the Royal Statistical Society, Series B, 63:727-744, 2001.
    • 6. F. Audrino and P. Bu¨hlmann. Volatility estimation with functional gradient descent for very high-dimensional financial time series. Journal of Computational Finance, 6(3):1- 26, 2003.
    • 7. F. Audrino and F. Corsi. Modeling tick-by-tick realized correlations. Computational Statistics & Data Analysis, 54:2373-2383, 2010.
    • 8. F. Audrino and F. Trojani. Estimating and predicting multivariate volatility regimes in global stock markets. Journal of Applied Econometrics, 21:345-369, 2006.
    • 9. F.M. Bandi and J.R. Russell. Realized covariation, realized beta and microstructure noise. Unpublished paper, Graduate School of Business, University of Chicago, 2005.
    • 10. A. Banerjee and G. Urga. Modelling structural breaks, long memory and stock market volatility: An overview. Journal of Econometrics, 129(1-2):1-34, 2005.
    • 11. O. E. Barndorff-Nielsen, P. R. Hansen, A. Lunde, and N. Shephard. Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Working Paper Series, 2010.
    • 12. O. E. Barndorff-Nielsen and N. Shephard. Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2:1-48, 2004.
    • 13. O.E. Barndorff-Nielsen, P.R. Hansen, A. Lunde, and N. Shephard. Designing realized kernels to measure the ex post variation of equity prices in the presence of noise. Econometrica, 76(6):1481-1536, 2008.
    • 14. O.E. Barndorff-Nielsen and N. Shephard. Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2):167-241, 2001.
    • 15. O.E. Barndorff-Nielsen and N. Shephard. Measuring the impact of jumps in multivariate price processes using bipower covariation. 2005.
    • 16. D. Bates. Post-'87 crash fears in the S&P 500 futures option market. Journal of Econometrics, 94:181-238, 2000.
    • 17. G.H. Bauer and K. Vorkink. Forecasting multivariate realized stock market volatility. Journal of Econometrics, 2010.
    • 18. S. Bianco, F. Corsi, and R. Reno`. Intraday LeBaron effects. Proceedings of the National Academy of Science of the USA, 106:11439-11443, 2009.
    • 19. T. Bollerslev, J. Litvinova, and G. Tauchen. Leverage and volatility feedback effects in high-frequency data. Journal of Financial Econometrics, 4(3):353, 2006.
    • 20. T Bollerslev, G Tauchen, and H Zhou. Expected stock returns and variance risk premia. Review of Financial Studies, 22(11):4463-4492, 2008.
    • 21. T. Bollerslev and J.H. Wright. Volatility forecasting, high-frequency data, and frequency domain inference. Review of Economics and Statistics, 83:596-602, 2001.
    • 22. T. Bollerslev and B. Y. B. Zhang. Measuring and modeling systematic risk in factor pricing models using high-frequency data. Journal of Empirical Finance, 10(5):533-558, 2003.
    • 23. M. Bonato, M. Caporin, and A. Ranaldo. Forecasting realized (co)variances with a block structure Wishart autoregressive model. Working Papers, 2009.
    • 24. K. Boudt, C. Croux, and S. Laurent. Outlyingness weighted quadratic covariation. Working Paper, 2008.
    • 25. L. Breiman, J. Friedman, C. J. Stone, and Richard A. Olshen. Classification and regression trees. Chapman & Hall/CRC, 1984.
    • 26. L.E. Calvet and A.J. Fisher. How to forecast long-run volatility: Regime switching and the estimation of multifractal processes. Journal of Financial Econometrics, 2(1):49-83, 2004.
    • 27. J. Y. Campbell and L. Hentschel. No news is good news: a asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31:281-318, 1992.
    • 28. R. Chiriac and V. Voev. Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics, 2010.
    • 29. K. Christensen, R. Oomen, and M. Podolskij. Realised quantile-based estimation of the integrated variance. Journal of Econometrics, 159(1):74-98, 2010.
    • 30. A. Christie. The stochastic behavior of common stock variances: value, leverage and interest rate effects. Journal of Financial Economics, 10:407-432, 1982.
    • 31. M.P. Clements, A.B. Galva˜o, and J.H. Kim. Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. Journal of Empirical Finance, 15(4):729-750, 2008.
    • 32. K. Cohen, G. A. Hawawini, S. F. Maier, R. Schwartz R., and D. Whitcomb D. Friction in the trading process and the estimation of systematic risk. Journal of Financial Economics, 12:263-278, 1983.
    • 33. F. Comte and E. Renault. Long memory in continuous-time stochastic volatility models. Mathematical Finance, 8(4):291-323, 1998.
    • 34. F. Corsi. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7:174-196, 2009.
    • 35. F. Corsi and F. Audrino. Realized covariance tick-by-tick in presence of rounded time stamps and general microstructure effects. Unpublished manuscript, University of St. Gallen, 2008.
    • 36. F. Corsi, D. La Vecchia, and N. Fusari. Realizing smiles: Pricing options with realized volatility. Working Paper, 2010.
    • 37. F. Corsi, S. Mittnik, C. Pigorsch, and U. Pigorsch. The volatility of realized volatility. Econometric Reviews, 27(1-3):1-33, 2008.
    • 38. F. Corsi, D. Pirino, and R. Reno`. Threshold bipower variation and the impact of jumps on volatility forecasting. Journal of Econometrics, 159:276-288, 2010.
    • 39. F. Corsi and R. Renò. Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling. Working Paper, 2010.
    • 40. G. Curci and F. Corsi. Discrete sine transform for multi-scales realized volatility measures. Quantitative Finance, 2010. Forthcoming.
    • 41. M.M. Dacorogna, U.A. Mu¨ller, R.D. Davé, R.B. Olsen, and O.V. Pictet. Modelling shortterm volatility with garch and harch models. In "Nonlinear Modelling of High Frequency Financial Time Series", pages 161-76, 1998. edited by C. Dunis and B. Zhou, John Wiley, Chichester.
    • 42. F. De Jong and T. Nijman. High frequency analysis of lead-lag relationships between financial markets. Journal of Empirical Finance, 4(2-3):259-277, 1997.
    • 43. R.F. Engle and G.G.J. Lee. A permanent and transitory component model of stock return volatility. In R.F. Engle and H. White, editors, Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive WJ Granger, pages 475-497. Oxford University Press, Oxford, 1999.
    • 44. T. Epps. Comovements in stock prices in the very short run. Journal of the American Statistical Association, 74:291-298, 1979.
    • 45. L. Forsberg and E. Ghysels. Why do absolute returns predict volatility so well? Journal of Financial Econometrics, 5:31-67, 2007.
    • 46. C. Francq and J.M. Zakoian. Bartlett's formula for a general class of nonlinear processes. Journal of Time Series Analysis, 30(4):449-465, 2009.
    • 47. E. Ghysels, P. Santa-Clara, and R. Valkanov. Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1-2):59-95, 2006.
    • 48. E. Ghysels, A. Sinko, and R. Valkanov. Midas regressions: Further results and new directions. Econometric Reviews, 26:53-90, 2006.
    • 49. R. Giacomini and H. White. Tests of conditional predictive ability. Econometrica, 74(6):1545-1578, 2006.
    • 50. L. Glosten, R. Jagannathan, and D. Runkle. On the relation between the expected value of the volatility of the nominal excess return on stocks. Journal of Finance, 48:1779-1801, 1989.
    • 51. S. Gonc¸alves and N. Meddahi. Box-Cox transforms for realized volatility. Journal of Econometrics, 160(1):129-144, 2011.
    • 52. C. Granger. Long memory relationships and the aggregation of dynamic models. Journal of Econometrics, 14:227-238, 1980.
    • 53. J.E. Griffin and R.C.A. Oomen. Covariance measurement in the presence of nonsynchronous trading and market microstructure noise. Journal of Econometrics, 160(1):58- 68, 2011.
    • 54. T. Hayashi and N. Yoshida. On covariance estimation of non-synchronously observed diffusion processes. Bernoulli, 11(2):359, 2005.
    • 55. J. Jacod, Y. Li, P.A. Mykland, M. Podolskij, and M. Vetter. Microstructure noise in the continuous case: The pre-averaging approach. Stochastic Processes and their Applications, 119(7):2249-2276, 2009.
    • 56. G.J. Jiang and R.C.A. Oomen. Testing for jumps when asset prices are observed with noise-a "swap variance" approach. Journal of Econometrics, 144(2):352-370, 2008.
    • 57. X. Jin and J. Maheu. Modelling realized covariances and returns. Working Papers, 2010.
    • 58. B. LeBaron. Stochastic volatility as a simple generator of financial power-laws and long memory. Quantitative Finance, 1:62131, 2001.
    • 59. S.S. Lee and P.A. Mykland. Jumps in financial markets: A new nonparametric test and jump dynamics. Review of Financial studies, 21(6):2535, 2008.
    • 60. A. Lo and W. Andrew. An econometric analysis of nonsynchronous trading. Journal of Econometrics, 45(1-2):181-211, 1990.
    • 61. P. Lynch and G. Zumbach. Market heterogeneities and the causal structure of volatility. Quantitative Finance, 3(4):320-331, 2003.
    • 62. C. Mancini. Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps. Scandinavian Journal of Statistics, 36(2):270-296, 2009.
    • 63. C. Mancini and R. Reno`. Threshold estimation of Markov models with jumps and interest rate modeling. Journal of Econometrics, 160(1):77-92, 2011.
    • 64. M.E. Mancino and S. Sanfelici. Estimating covariance via Fourier method in the presence of asynchronous trading and microstructure noise. Journal of Financial Econometrics, 2010. Forthcoming.
    • 65. A. McAleer and M.C. Medeiros. A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries. Journal of Econometrics, 147:104-119, 2008.
    • 66. U. Muller, M. Dacorogna, R. Dave´, R. Olsen, O. Pictet, and J. von Weizsacker. Volatilities of different time resolutions - analyzing the dynamics of market components. Journal of Empirical Finance, 4:213-239, 1997.
    • 67. A. Palandri. Consistent realized covariance for asynchronous observations contaminated by market microstructure noise. Unpublished Manuscript, 2006.
    • 68. R. Reno`. A closer look at the Epps effect. International Journal of Theoretical and Applied Finance, 6(1):87-102, 2003.
    • 69. M. Scharth and M.C. Medeiros. Asymmetric effects and long memory in the volatility of dow jones stocks. International Journal of Forecasting, 25:304-327, 2009.
    • 70. M. Scholes and J. Williams. Estimating betas from nonsynchronous data. Journal of Financial Economics, 5:181-212, 1977.
    • 71. K. Sheppard. Realized covariance and scrambling. Unpublished Manuscript, 2006.
    • 72. V. Voev and A. Lunde. Integrated covariance estimation using high-frequency data in the presence of noise. Journal of Financial Econometrics, 5:68-104, 2007.
    • 73. L. Zhang. Efficient estimation of stochastic volatility using noisy observations: a multiscale approach. Bernoulli, 12(6):1019-1043, 2006.
    • 74. L. Zhang, P. A. Mykland, and Y. A¨ıt-Sahalia. A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100:1394-1411, 2005.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article