Barndor-Nielsen, O. and N. Shephard (2000); Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in nancial economics, J. Royal Statistical Society, Series B, 63, 1-42.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in this paper is the inclusion of stochastic volatility in the dynamics of the risky asset. The model we use is driven by a superposition of non Gaussian Ornstein Uhlenbeck processes and it was recently proposed and intensively investigated for real market data by Barndor Nielsen and Shephard [3]. Using the dynamic programming method, explicit trading strategies and expressions for the value function via Feynman Kac formulas are derived and veri ed for power utilities. Some numerical examples are also presented. 1. Introduction Recently, Barndor Nielsen and Shephard [3] suggested to ....

....and Shephard [3] Using the dynamic programming method, explicit trading strategies and expressions for the value function via Feynman Kac formulas are derived and veri ed for power utilities. Some numerical examples are also presented. 1. Introduction Recently, Barndor Nielsen and Shephard [3] suggested to model the volatility in asset price dynamics as a weighted sum of non Gaussian Ornstein Uhlenbeck processes. This volatility model possesses many of the observed features of nancial logreturn data, such as heavy tails and longrange dependency. Barndo Nielsen and Shephard [3] ....

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O. E. Barndor-Nielsen and N. Shephard, Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in nancial economics, J. R. Statist. Soc. B 63 (2001). With discussion.

.... reveals that, under suitable conditions, realized volatility is not only an unbiased ex post estimator of daily return volatility, but also asymptotically free of measurement error, as discussed in Andersen, Bollerslev, Diebold and Labys (2001a) henceforth ABDL) as well as concurrent work by BarndorffNielsen and Shephard (2000, 2001). Building on the notion of continuous time arbitrage free price processes, we progress in several directions, including more rigorous theoretical foundations, multivariate emphasis, and links to modern risk management. Empirically, by treating the volatility as observed rather than latent, our ....

Barndorff-Nielsen, O.E. and N. Shephard (2001), "Non-Gaussian Ornstein-Uhlenbeck-Based Models and Some of Their Uses in Financial Economics," Journal of the Royal Statistical Society, Series B, in press.

....for long memory may be consistent with a short memory process that is the sum of a small number of components whose spectral density happens to resemble that of a long memory process except at extremely low frequencies. The long memory specification may then provide a much more parsimonious model. Barndorff Nielsen and Shephard (2001) model volatility in continuous time as the sum of a few short memory components. Their analysis of ten years of 5 minute DM returns, adjusted for intraday volatility periodicity, shows that the sum of four volatility processes is able to provide an excellent match to the autocorrelations of ....

.... (Andersen, Bollerslev, Diebold and Labys, 2000) This evidence may more precisely be interpreted as evidence for long memory effects, because there are short memory processes that have similar autocorrelations and spectral densities, except at very low frequencies (Gallant, Hsu and Tauchen, 1999, Barndorff Nielsen and Shephard, 2001). There is also evidence that people trade at option prices that are more compatible with a long memory process for volatility than with a parsimonious short memory process (Bollerslev and Mikkelsen, 1999) 37 The theory of option pricing when volatility follows a discrete time ARCH process ....

Barndorff-Nielsen, O.E. and N. Shephard, 2001, Non-Gaussian Ornstein-Uhlenbeck based models and some of their uses in financial economics, Journal of the Royal Statistical Society B, forthcoming.

....stock price by a geometric normal invers Gaussian L evy process, the long range dependency structure observed in data is not explained. Even though the geometric normal invers Gaussian L evy process gives a good description for the marginals, new models are called for. Barndor Nielsen and Shepard [5] (see also [6] and [4] have recently suggested a class of stochastic volatility models where the risky asset follows the dynamics d ln S t = t dt p t dW t (4.1) and d t = t dt dL t ; 4.2) L t being a driftless pure jump L evy process with non negative increments ....

O. E. Barndor-Nielsen and N. Shephard, Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in nancial economics. To appear in J. R. Statist. Soc. B.

....a model for the deformation of the implied volatility suface. This correspondence may be, however, very non explicit [23, 35] It would be nevertheless interesting to compare our empirical ndings with the dynamics of implied volatility surfaces in traditional stochastic volatility models [23, 4] at least on a qualitative or numerical basis. 7.3 Quantifying and hedging volatility risk Our model allows a simple and straightforward approach to the modeling and hedging of volatility risk, de ned in terms familiar to practitioners in the options market, namely that of Vega risk de ned via ....

Barndor-Nielsen, O.E. & Shephard, N. (2001) Non-Gaussian Ornstein{ Uhlenbeck-based models and some of their uses in nancial economics (with discussion), Journal of the Royal Statistical Society, Series B, 63, 167-241.

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Barndor-Nielsen, O. and N. Shephard (2000); Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in nancial economics, J. Royal Statistical Society, Series B, 63, 1-42.

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Barndor-Nielsen, O. E. & Shephard, N. (2001). Non-Gaussian Ornstein-Uhlenbeck based models and some of their uses in nancial economics (with discussion), J. Roy. Statist. Soc. Ser. B 63: 167-241.

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Barndor-Nielsen, O.E. and Shephard, N. (2001) Non-Gaussian Ornstein{Uhlenbeckbased models and some of their uses in nancial economics (with discussion). J. R. Statist. Soc. Ser. B 63, 167-241.

No context found.

Barndor#-Nielsen, O. E. and N. Shephard (2002). Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics (with discussion). Journal of the Royal Statistical Society B 63, 167--241.

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Barndor-Nielsen, O.E. and Shephard, N. (2001) Non-Gaussian Ornstein{Uhlenbeckbased models and some of their uses in nancial economics (with discussion). J. Royal Statist. Soc., Ser. B 63, 167-241.

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Barndor#-Nielsen, N. and N. Shephard (2001b). Non-Gaussian Ornstein-- Uhlenbeck-based models and some of their uses in financial economics. Journal of the Royal Statistical Society, Series B 63, 167--241.

No context found.

Barndor#-Nielsen, N. and N. Shephard (2001). Non-Gaussian Ornstein--Uhlenbeckbased models and some of their uses in financial economics. Journal of the Royal Statistical Society, Series B 63, 167--241.

No context found.

O. E. Barndor#-Nielsen and N. Shephard. Non-Gaussian OrnsteinUhlenbeck -based models and some of their uses in financial economics (with discussion). Journal of the Royal Statistical Society, Series B, 63:167--241, 2001.

No context found.

O. E. Barndor#-Nielsen and N. Shephard. Non-Gaussian OrnsteinUhlenbeck -based models and some of their uses in financial economics (with discussion). Journal of the Royal Statistical Society, Series B, 63:167--241, 2001b.

No context found.

Barndor-Nielsen, O. E. and Shephard, N. (2001) Non-Gaussian OrnsteinUhlenbeck -based models and some of their uses in nancial economics. J.R. Statist. Soc. B, 63, 1-42

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