IDEAS home Printed from https://ideas.repec.org/r/bes/jnlbes/v19y2001i1p44-54.html
   My bibliography  Save this item

On the Normal Inverse Gaussian Stochastic Volatility Model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Hautsch, Nikolaus & Voigt, Stefan, 2019. "Large-scale portfolio allocation under transaction costs and model uncertainty," Journal of Econometrics, Elsevier, vol. 212(1), pages 221-240.
  2. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
  3. Yang Minxian, 2011. "Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-21, May.
  4. Song, Shijia & Li, Handong, 2022. "Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution," International Review of Financial Analysis, Elsevier, vol. 82(C).
  5. 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.
  6. N. Balakrishna & Bovas Abraham & Ranjini Sivakumar, 2006. "Gamma stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 153-171.
  7. Saralees Nadarajah, 2012. "Models for stock returns," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 411-424, February.
  8. Ruiz, Esther & Veiga, Helena, 2008. "Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2846-2862, February.
  9. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
  10. Javed Farrukh & Podgórski Krzysztof, 2017. "Tail Behavior and Dependence Structure in the APARCH Model," Journal of Time Series Econometrics, De Gruyter, vol. 9(2), pages 1-48, July.
  11. José Antonio Núñez-Mora & Roberto Joaquín Santillán-Salgado & Mario Iván Contreras-Valdez, 2022. "COVID Asymmetric Impact on the Risk Premium of Developed and Emerging Countries’ Stock Markets," Mathematics, MDPI, vol. 10(9), pages 1-36, April.
  12. Krämer, Walter & Messow, Philip, 2012. "Structural Change and Spurious Persistence in Stochastic Volatility," Ruhr Economic Papers 310, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  13. Lars Forsberg & Tim Bollerslev, 2002. "Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH-NIG model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 535-548.
  14. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
  15. Homm, Ulrich & Pigorsch, Christian, 2012. "Beyond the Sharpe ratio: An application of the Aumann–Serrano index to performance measurement," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2274-2284.
  16. repec:zbw:rwirep:0310 is not listed on IDEAS
  17. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
  18. Jouchi Nakajima & Yasuhiro Omori, 2010. "Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution Models," CIRJE F-Series CIRJE-F-738, CIRJE, Faculty of Economics, University of Tokyo.
  19. Lars Forsberg & Anders Eriksson, 2004. "The Mean Variance Mixing GARCH (1,1) model," Econometric Society 2004 Australasian Meetings 323, Econometric Society.
  20. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
  21. Rehim Kilic, 2011. "A conditional variance tale from an emerging economy's freely floating exchange rate," Applied Economics, Taylor & Francis Journals, vol. 43(19), pages 2465-2480.
  22. Papantonis Ioannis & Rompolis Leonidas S. & Tzavalis Elias & Agapitos Orestis, 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.
  23. Walter Krämer & Philip Mess, 2012. "Structural Change and Spurious Persistence in Stochastic Volatility," Ruhr Economic Papers 0310, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  24. Guo, Zi-Yi, 2017. "Empirical Performance of GARCH Models with Heavy-tailed Innovations," EconStor Preprints 167626, ZBW - Leibniz Information Centre for Economics.
  25. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
  26. Patricia Lengua Lafosse & Cristian Bayes & Gabriel Rodríguez, 2015. "A Stochastic Volatility Model with GH Skew Student’s t-Distribution: Application to Latin-American Stock Returns," Documentos de Trabajo / Working Papers 2015-405, Departamento de Economía - Pontificia Universidad Católica del Perú.
  27. Nakajima Jouchi, 2013. "Stochastic volatility model with regime-switching skewness in heavy-tailed errors for exchange rate returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 499-520, December.
  28. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
  29. Zi-Yi Guo, 2017. "GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns," International Journal of Business and Management, Canadian Center of Science and Education, vol. 12(9), pages 1-28, August.
  30. Pentti Saikkonen & Markku Lanne, 2004. "A Skewed GARCH-in-Mean Model: An Application to U.S. Stock Returns," Econometric Society 2004 North American Summer Meetings 469, Econometric Society.
  31. Lengua Lafosse, Patricia & Rodríguez, Gabriel, 2018. "An empirical application of a stochastic volatility model with GH skew Student's t-distribution to the volatility of Latin-American stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 155-173.
  32. Guo, Zi-Yi, 2022. "Risk management of Bitcoin futures with GARCH models," Finance Research Letters, Elsevier, vol. 45(C).
  33. Lance Kent & Toan Phan, 2019. "Time-Varying Skewness and Real Business Cycles," Economic Quarterly, Federal Reserve Bank of Richmond, issue 2Q, pages 59-103.
  34. Bruno Ebner & Bernhard Klar & Simos G. Meintanis, 2018. "Fourier inference for stochastic volatility models with heavy-tailed innovations," Statistical Papers, Springer, vol. 59(3), pages 1043-1060, September.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.