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Articles

Box–Cox realized asymmetric stochastic volatility models with generalized Student's t-error distributions

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Pages 1906-1927 | Received 11 Nov 2014, Accepted 25 Nov 2015, Published online: 30 Dec 2015

References

  • T.G. Andersen, T. Bollerslev, F.X. Diebold, and P. Labys, The distribution of realized exchange rate volatility, J. Am. Stat. Assoc. 96(453) (2001), pp. 42–55. doi: 10.1198/016214501750332965
  • O.E. Barndorff-Nielsen, P.R. Hansen, A. Lunde, and N. Shephard, Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise, Econometrica 76(6) (2008), pp. 1481–1536. doi: 10.3982/ECTA6495
  • O.E. Barndorff-Nielsen and N. Shephard, Power and bipower variation with stochastic volatility and jumps, J. Financ. Econ. 2(1) (2004), pp. 1–37.
  • T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, J. Econ. 31 (1986), pp. 307–327. doi: 10.1016/0304-4076(86)90063-1
  • M.A. Carnero, D. Peña, and E. Ruiz, Is Stochastic Volatility More Flexible than GARCH?, Working Paper 01-08, Universidad Carlos III de Madrid, 2001.
  • P. Christoffersen, B. Feunou, K. Jacobs, and N. Meddahi, The Economic Value of Realized Volatility: Using High-frequency Returns for Option Valuation, Working Paper 2012-34, Bank of Canada, 2012.
  • D. Dobrev and P. Szerszen, The Information Content of High-frequency Data for Estimating Equity Return Models and Forecasting Risk, Working Paper, Finance and Economics Discussion Series, 2010.
  • R.F. Engle, Autoregressive conditional heteroskedasticity with estimates of the variance of the united kingdom inflation, Econometrica 50 (1982), pp. 987–1007. doi: 10.2307/1912773
  • A.E. Gelfand and D.K. Dey, Bayesian model choice: Asymptotics and exact calculations, J. R. Stat. Soc. Ser. B 56(3) (1994), pp. 501–514.
  • J. Geweke, Contemporary Bayesian Econometrics and Statistics, John Wiley & Sons, Hoboken, 2005.
  • E. Ghysels, A.C. Harvey, and E. Renault, Stochastic volatility, in Handbook of Statistics: Statistical Methods in Finance, G.S. Maddala and C.R. Rao, eds., Elsevier Science, Amsterdam, 1996, pp. 119–191.
  • W.R. Gilks, Derivative-free adaptive rejection sampling for Gibbs sampling, in Bayesian Statistics, J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, eds., Vol. 4, Oxford University Pres, Oxford, 1992, pp. 169–193.
  • W.R. Gilks and P. Wild, Adaptive rejection sampling for Gibbs sampling, Appl. Stat. 41(2) (1992), pp. 337–348. doi: 10.2307/2347565
  • M. Girolami and B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, J. R. Stat. Soc. Ser. B 73(2) (2011), pp. 1–37. doi: 10.1111/j.1467-9868.2010.00765.x
  • S. Gonçalves and N. Meddahi, Box–Cox transforms for realized volatility, J. Econ. 160 (2011), pp. 129–144. doi: 10.1016/j.jeconom.2010.03.026
  • R.E. Kass and A.E. Raftery, Bayes factors, J. Am. Stat. Assoc. 90(430) (1995), pp. 773–795. doi: 10.1080/01621459.1995.10476572
  • S. Kim, N. Shephard, and S. Chib, Stochastic volatility: Likelihood inference and comparison with ARCH models, in Stochastic Volatility: Selected Readings, N. Shephard, ed., Oxford University Press, New York, 2005, pp. 283–322.
  • S.J. Koopman and M. Scharth, The analysis of stochastic volatility in the presence of daily realized measures, J. Financ. Econ. 11(1) (2013), pp. 76–115.
  • Y.-C. Ku, P. Bloomfield, and S.K. Ghosh, A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix, Stat. Model. 14(1) (2014), pp. 1–20. doi: 10.1177/1471082X13490016
  • J. Nakajima and Y. Omori, Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's t-distribution, Comput. Stat. Data Anal. 56 (2012), pp. 3690–3704. doi: 10.1016/j.csda.2010.07.012
  • R.M. Neal, MCMC using Hamiltonian dynamics, in Handbook of Markov Chain Monte Carlo, S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, eds., Chapman & Hall/CRC Press, Boca Raton, 2011, pp. 113–162.
  • D. Noureldin, N. Shephard, and K. Sheppard, Multivariate high-frequency-based volatility (heavy) models, J. Appl. Econ. 27(6) (2012), pp. 907–933. doi: 10.1002/jae.1260
  • D.B. Nugroho and T. Morimoto, Realized non-linear stochastic volatility models with asymmetric effects and generalized Student's t-distributions, J. Jpn. Stat. Soc. 44(1) (2014), pp. 83–118. doi: 10.14490/jjss.44.83
  • D.B. Nugroho and T. Morimoto, Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-based methods, Comput. Stat. 30(2) (2015), pp. 491–516. doi: 10.1007/s00180-014-0546-6
  • M. Takahashi, Y. Omori, and T. Watanabe, Estimating stochastic volatility models using daily returns and realized volatility simultaneously, Comput. Stat. Data Anal. 53 (2009), pp. 2404–2426. doi: 10.1016/j.csda.2008.07.039
  • M. Takahashi, Y. Omori, and T. Watanabe, Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution, Working Paper Series CIRJE-F-921, CIRJE, Faculty of Economics, University of Tokyo, 2014.
  • S.J. Taylor, Financial returns modelled by the product of two stochastic processes study of the daily sugar prices 1961–75, in Stochastic Volatility: Selected Readings, N. Shephard, ed., Oxford University Press, New York, 2005, pp. 60–82.
  • G. Tsiotas, On the use of non-linear transformations in stochastic volatility models, Stat. Methods Appl. 18 (2009), pp. 555–583. doi: 10.1007/s10260-008-0113-9
  • G. Tsiotas, On generalized asymmetric stochastic volatility models, Comput. Stat. Data Anal. 56 (2012), pp. 151–172. doi: 10.1016/j.csda.2011.06.031
  • J. Yu, Forecasting volatility in the New Zealand stock market, Appl. Financ. Econ. 12 (2002), pp. 193–202. doi: 10.1080/09603100110090118
  • J. Yu, Z. Yang, and X. Zhang, A class of nonlinear stochastic volatility models and its implications for pricing currency options, Comput. Stat. Data Anal. 51 (2006), pp. 2218–2231. doi: 10.1016/j.csda.2006.08.024
  • L. Zhang, P.A. Mykland, and Y. Aït-Sahalia, A tale of two time scales: Determining integrated volatility with noisy high-frequency data, J. Am. Stat. Assoc. 100(472) (2005), pp. 1394–1411. doi: 10.1198/016214505000000169
  • X. Zhang and M.L. King, Box–Cox stochastic volatility models with heavy-tails and correlated errors, J. Empir. Financ. 15(3) (2008), pp. 549–566. doi: 10.1016/j.jempfin.2007.05.002

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