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Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions

Listed author(s):
  • Yang Minxian

    ()

    (The University of New South Wales)

The mixture structure of the generalized hyperbolic distribution of Barndorff-Nielsen (1997) is explored to quantify the contemporaneous correlation between return and volatility and to identify the effects of volatility feedback and risk premium within GARCH models. The statistical analysis of the excess returns based on the CRSP value-weighted portfolio index supports both volatility feedback and risk premium theories.

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File URL: https://www.degruyter.com/view/j/snde.2011.15.3/snde.2011.15.3.1820/snde.2011.15.3.1820.xml?format=INT
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Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 15 (2011)
Issue (Month): 3 (May)
Pages: 1-21

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Handle: RePEc:bpj:sndecm:v:15:y:2011:i:3:n:6
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  1. Hentschel, Ludger & Campbell, John, 1992. "No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns," Scholarly Articles 3220232, Harvard University Department of Economics.
  2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  4. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
  5. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
  6. Andersson, Jonas, 2001. "On the Normal Inverse Gaussian Stochastic Volatility Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 44-54, January.
  7. Karlis, Dimitris, 2002. "An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution," Statistics & Probability Letters, Elsevier, vol. 57(1), pages 43-52, March.
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