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Citations for "Why Do Absolute Returns Predict Volatility So Well?"

by Lars Forsberg & Eric Ghysels

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  1. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
  2. Nima Nonejad, 2013. "A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory," CREATES Research Papers 2013-24, Department of Economics and Business Economics, Aarhus University.
  3. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, 01.
  4. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," Studies in Economics 1405, School of Economics, University of Kent.
  5. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
  6. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "“Financial stress transmission in EMU sovereign bond market volatility: a connectedness analysis”," IREA Working Papers 201510, University of Barcelona, Research Institute of Applied Economics, revised Feb 2015.
  7. repec:lan:wpaper:3046 is not listed on IDEAS
  8. Chun Liu & John M Maheu, 2010. "Intraday Dynamics of Volatility and Duration: Evidence from the Chinese Stock Market," Working Papers tecipa-401, University of Toronto, Department of Economics.
  9. Chai, Edwina F.L. & Lee, Adrian D. & Wang, Jianxin, 2015. "Global information distribution in the gold OTC markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 206-217.
  10. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
  11. Hansen, Peter R. & Lunde, Asger, 2014. "Estimating The Persistence And The Autocorrelation Function Of A Time Series That Is Measured With Error," Econometric Theory, Cambridge University Press, vol. 30(01), pages 60-93, February.
  12. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, 08.
  13. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
  14. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
  15. Zhang, Bing & Wang, Peijie, 2014. "Return and volatility spillovers between china and world oil markets," Economic Modelling, Elsevier, vol. 42(C), pages 413-420.
  16. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany.
  17. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
  18. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
  19. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
  20. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
  21. repec:hal:journl:peer-00741630 is not listed on IDEAS
  22. Antonakakis, Nikolaos & Kizys, Renatas, 2015. "Dynamic spillovers between commodity and currency markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 303-319.
  23. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
  24. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
  25. repec:lan:wpaper:592830 is not listed on IDEAS
  26. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
  27. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
  28. Francesco Audrino & Yujia Hu, 2016. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 8, February.
  29. Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
  30. Ghysels, Eric & Sohn, Bumjean, 2009. "Which power variation predicts volatility well?," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 686-700, September.
  31. Tsiakas, Ilias, 2008. "Overnight information and stochastic volatility: A study of European and US stock exchanges," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 251-268, February.
  32. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
  33. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
  34. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
  35. Christian T. Brownlees & Giampiero Gallo, 2007. "Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  36. Politis, Dimitris N & Thomakos, Dimitrios D, 2008. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," University of California at San Diego, Economics Working Paper Series qt982208kx, Department of Economics, UC San Diego.
  37. Dinghai Xu & John Knight, 2013. "Stochastic volatility model under a discrete mixture-of-normal specification," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 216-239, April.
  38. Nowak, Sylwia & Andritzky, Jochen & Jobst, Andreas & Tamirisa, Natalia, 2011. "Macroeconomic fundamentals, price discovery, and volatility dynamics in emerging bond markets," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2584-2597, October.
  39. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," Ifo Schnelldienst, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
  40. Mohamed Boutahar, 2010. "Behaviour of skewness, kurtosis and normality tests in long memory data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 193-215, June.
  41. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
  42. Shuichi Nagata, 2012. "Consistent Estimation of Integrated Volatility Using Intraday Absolute Returns for SV Jump Diffusion Processes," Economics Bulletin, AccessEcon, vol. 32(1), pages 306-314.
  43. Fulvio Corsi & Davide Pirino & Roberto Reno, 2009. "Volatility Forecasting: The Jumps Do Matter," Global COE Hi-Stat Discussion Paper Series gd08-036, Institute of Economic Research, Hitotsubashi University.
  44. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
  45. Alexander Subbotin & Thierry Chauveau & Kateryna Shapovalova, 2009. "Volatility Models : from GARCH to Multi-Horizon Cascades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00390636, HAL.
  46. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
  47. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
  48. repec:lan:wpaper:3324 is not listed on IDEAS
  49. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, 09.
  50. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 15(3), pages 94-138.
  51. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, 07.
  52. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
  53. Dimitrios P. Louzis & Spyros Xanthopoulos-Sisinis & Apostolos P. Refenes, 2012. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Applied Economics, Taylor & Francis Journals, vol. 44(27), pages 3533-3550, September.
  54. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
  55. Dimitris N. Politis & Dimitrios D. Thomakos, 2007. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," Working Paper Series 44_07, The Rimini Centre for Economic Analysis.
  56. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
  57. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
  58. Giovanni De Luca & Giampiero Gallo, 2010. "A Time-varying Mixing Multiplicative Error Model for Realized Volatility," Econometrics Working Papers Archive wp2010_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  59. Mike So & Rui Xu, 2013. "Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data," Asia-Pacific Financial Markets, Springer, vol. 20(1), pages 83-111, March.
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