How accurate is the asymptotic approximation to the distribution of realised variance

By Ole E. Barndorff-Nielsen, N. Shephard. 2001

68 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7751e1adf3e839c0e52d78b0126330f3e251caa7

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Citations in the corpus

Some recent developments in stochastic volatility modelling (2002, methodology): “This is also shown in Barndorff-Nielsen and Shephard (2001c).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “The bias (37) continues to hold for non-Gaussian OU processes of the type discussed by Barndorff-Nielsen and Shephard (2001).”
Power and bipower variation with stochastic volatility and jumps (2003, background): “This is relevant when the σ process exhibits jumps (e.g. Barndorff-Nielsen and Shephard (2001) and Eraker, Johannes, and Polson (2003)).”
Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance (2007, background): “The central limit theorem (CLT) (44) is based on the asymptotic theory of Barndor-Nielsen and Shephard (2005) , which involves a finite sample correction (45) on some important earlier limit theory contributions made by Jacod (1994) and Barndor-Nielsen and Shephard (2002).”
BOOTSTRAPPING REALIZED VOLATILITY (2009, background): “The results follow from the definition of q1 (x) and q� 1 (x) in ( 12 ) and (14), respectively, given the cumulants expansions in Theorems A.1, A.2 and A.3.”
Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities (2005, methodology): “11 The accuracy of (6) and the corresponding CLT for RVt(h)1/2 and log(RVt(h)) based on the -method has also previously been investigated by Barndor-Nielsen and Shephard (2003) .”
Breaks and persistency: macroeconomic causes of stock market volatility (2006, result): “A similar asymptotic distribution holds for the realized log variance estimator, which, according to the results of Barndorff-Nielsen and Shephard [12], provides a more accurate asymptotic approximation.”
Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH‐NIG model (2002, background): “…realized volatilities are subject to measurement error vis-à-vis the true daily latent volatilities (see, for instance, Andreou and Ghysels, 2002; Barndorff-Nielsen and Shephard, 2001b, 2002b; Meddahi, ‘A theoretical comparison between integrated and realized volatilities’, unpublished…”
Econometric analysis of realized volatility and its use in estimating stochastic volatility models (2002, methodology): “This is discussed in the OU case in Barndorff-Nielsen and Shephard (2001a) and in more detail by Barndorff-Nielsen (2001) who formalizes the use of superpositions as a way of modelling long-range dependence.”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “O t h e r M e t h o d s o f V o l a t i l i t y M o d e l i n g “Stochastic volatility” models (Barndorff-Nielsen et al. (2001), Chib et al. (1998), Ghysels et al. (1996), Harvey and Shephard (1993), Jacquier et al. (1994), Shephard (1996), Taylor (1994)), “implied volatility” models (Day and Lewis ”
Realised power variation and stochastic volatility models (2003, methodology): “These models have been developed in this context by Barndorff-Nielsen and Shephard (2001).”