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Citations for "Volatility Forecasting With Range-Based EGARCH Models"

by Brandt, Michael W. & Jones, Christopher S.

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  1. Miao, Daniel Wei-Chung & Wu, Chun-Chou & Su, Yi-Kai, 2013. "Regime-switching in volatility and correlation structure using range-based models with Markov-switching," Economic Modelling, Elsevier, vol. 31(C), pages 87-93.
  2. Vagnani, Gianluca, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 103-118, October.
  3. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
  4. 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.
  5. Reboredo, Juan C. & Ugando, Mikel, 2015. "Downside risks in EU carbon and fossil fuel markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 111(C), pages 17-35.
  6. S. Bhaumik & M. Karanasos & A. Kartsaklas, 2008. "Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market," William Davidson Institute Working Papers Series wp935, William Davidson Institute at the University of Michigan.
  7. Grosche, Stephanie & Heckelei, Thomas, 2014. "Directional Volatility Spillovers between Agricultural, Crude Oil, Real Estate and other Financial Markets," Discussion Papers 166079, University of Bonn, Institute for Food and Resource Economics.
  8. Wintenberger, Olivier & Cai, Sixiang, 2011. "Parametric inference and forecasting in continuously invertible volatility models," MPRA Paper 31767, University Library of Munich, Germany.
  9. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
  10. Camilo Serrano & Martin Hoesli, 2008. "Are Securitized Real Estate Returns More Predictable Than Stock Returns?," ERES eres2008_252, European Real Estate Society (ERES).
  11. Peter Christoffersen & Mathieu Fournier & Kris Jacobs & Mehdi Karoui, 2015. "Option-Based Estimation of the Price of Co-Skewness and Co-Kurtosis Risk," CREATES Research Papers 2015-54, Department of Economics and Business Economics, Aarhus University.
  12. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
  13. Wintenberger, Olivier, 2013. "Continuous invertibility and stable QML estimation of the EGARCH(1,1) model," MPRA Paper 46027, University Library of Munich, Germany.
  14. Harris, Richard D.F. & Stoja, Evarist & Yilmaz, Fatih, 2011. "A cyclical model of exchange rate volatility," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3055-3064, November.
  15. Tomasz Skoczylas, 2013. "Modelowanie i prognozowanie zmienności przy użyciu modeli opartych o zakres wahań," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 35.
  16. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.
  17. Yang-Ho Park & Nicole Abruzzo, 2016. "An Empirical Analysis of Futures Margin Changes: Determinants and Policy Implications," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(1), pages 65-100, February.
  18. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
  19. Kumar, Dilip & Maheswaran, S., 2014. "Modeling and forecasting the additive bias corrected extreme value volatility estimator," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 166-176.
  20. Jie Zhu, 2009. "Pricing volatility of stock returns with volatile and persistent components," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(3), pages 243-269, September.
  21. Gianluca Vagnani, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Post-Print hal-00736952, HAL.
  22. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
  23. Molnár, Peter, 2012. "Properties of range-based volatility estimators," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 20-29.
  24. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.
  25. Kumar, Dilip, 2015. "Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis," Economic Modelling, Elsevier, vol. 49(C), pages 354-371.
  26. Vladimir Tsenkov, 2009. "Financial Markets Modelling," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 87-96.
  27. Tomasz Skoczylas, 2015. "Log-volatility enhanced GARCH models for single asset returns," Bank i Kredyt, National Bank of Poland, Economic Institute, vol. 46(5), pages 411-432.
  28. Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard, 2012. "Forecasting volatility with asymmetric smooth transition dynamic range models," International Journal of Forecasting, Elsevier, vol. 28(2), pages 384-399.
  29. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
  30. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
  31. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
  32. Chen, Cathy W.S. & Gerlach, Richard & Lin, Edward M.H., 2008. "Volatility forecasting using threshold heteroskedastic models of the intra-day range," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2990-3010, February.
  33. Chou, Ray Yeutien & Cai, Yijie, 2009. "Range-based multivariate volatility model with double smooth transition in conditional correlation," Global Finance Journal, Elsevier, vol. 20(2), pages 137-152.
  34. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
  35. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "A No Arbitrage Fractional Cointegration Analysis Of The Range Based Volatility," CREATES Research Papers 2009-31, Department of Economics and Business Economics, Aarhus University.
  36. Chiu, Ching-Wai (Jeremy) & Harris, Richard & Stoja, Evarist & Chin, Michael, 2016. "Financial market volatility, macroeconomic fundamentals and investor sentiment," Bank of England working papers 608, Bank of England.
  37. Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.
  38. Ray Chou & Chun-Chou Wu & Nathan Liu, 2009. "Forecasting time-varying covariance with a range-based dynamic conditional correlation model," Review of Quantitative Finance and Accounting, Springer, vol. 33(4), pages 327-345, November.
  39. Tomasz Skoczylas, 2015. "Bivariate GARCH models for single asset returns," Working Papers 2015-03, Faculty of Economic Sciences, University of Warsaw.
  40. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
  41. Jie Zhu, 2008. "Pricing Volatility of Stock Returns with Volatile and Persistent Components," CREATES Research Papers 2008-14, Department of Economics and Business Economics, Aarhus University.
  42. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
  43. Chiang, Min-Hsien & Wang, Li-Min, 2011. "Volatility contagion: A range-based volatility approach," Journal of Econometrics, Elsevier, vol. 165(2), pages 175-189.
  44. 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.
  45. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
  46. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
  47. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, 01.
  48. Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
  49. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
  50. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
  51. Kumar, Dilip & Maheswaran, S., 2014. "A new approach to model and forecast volatility based on extreme value of asset prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 128-140.
  52. Shaen Corbet & Cian Twomey, 2014. "Have Exchange Traded Funds Influenced Commodity Market Volatility?," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 323-335.
  53. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
  54. 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.
  55. Petmezas, Dimitris & Santamaria, Daniel, 2014. "Investor induced contagion during the banking and European sovereign debt crisis of 2007–2012: Wealth effect or portfolio rebalancing?," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 401-424.
  56. Sin, Chor-Yiu (CY), 2013. "Using CARRX models to study factors affecting the volatilities of Asian equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 552-564.
  57. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
  58. 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.
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