Realised power variation and stochastic volatility models

By Ole E. Barndorff-Nielsen, N. Shephard. 2003

201 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/631a8c827a3c024d6fe7fe40c219f2ff9caa22e0

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Citations in the corpus

Pricing variance and volatility swaps in a stochastic volatility model with regime switching: discrete observations case (2013, background): “Barndorff-Nielsen and Shephard (2003) studied the theoretical properties of the average of the realized volatility (equation (4)) and found that the definition RVd2volð0,N,TeÞ is a more robust measurement of realized volatility.”
Power and Multipower Variation: inference for high frequency data (2006, methodology): “From Barndorff-Nielsen and Shephard [ 6 ] we know that the condition (12.16) on a is satisfied for the BNS model and for the special case r = s = 1 it can be dropped (cf.”
Estimating quadratic variation using realized variance (2002, methodology): “This result appeared in concurrent and independent work by Andersen and Bollerslev (1998) (assuming α(t) = 0) and Barndorff-Nielsen and Shephard (2001b).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “When r is an integer it has been studied from a probabilistic viewpoint by Jacod (1994) while Barndorff-Nielsen and Shephard (2003) look at the econometrics of the case where r > 0.”
Some recent developments in stochastic volatility modelling (2002, methodology): “This is also shown in Barndorff-Nielsen and Shephard (2001c).”
Power variation and stochastic volatility: a review and some new results (2004, methodology): “With a correct proof (see Barndorff-Nielsen and Shephard (2003c)), the additional condition is not needed.”
Bootstrapping realized multivariate volatility measures (2010, unknown): “It is easy to check that β̂ ∗ 12 defined in (9) converges in probability (under the bootstrap probability measure P ∗) to β̂12 = P1/h i=1 E ∗(y∗ 1iy 2i) P1/h i=1 E ∗(y∗2 2i ) .”
The Volatility of Realized Volatility (2005, methodology): “The asymptotic theory of this estimator is reproduced involving the notions of integrated quarticity and alternative quarticity measures, as introduced by Andersen et al. (2007, 2005) and Barndorff-Nielsen and Shephard (2003, 2004b, 2005, 2006).”
Estimation of Integrated Volatility in Continuous‐Time Financial Models with Applications to Goodness‐of‐Fit Testing (2006, result): “The work, which is most similar in spirit with the present paper are the papers of Corradi & White (1999), Kleinow (2002), Barndorff-Nielsen & Shephard (2003) and Woerner (2003), who also discuss the situation, where the time span is fixed and the length of the discrete sampling interval converges…”
Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, methodology): “HAR-RV: Our benchmark econometric model is the HAR–RV originally proposed by Corsi (2009) and used in several empirical studies (Andersen et al.”
MIDAS Regressions: Further Results and New Directions (2007, background): “…by Andersen et al. (2003) (and references cited therein). rt+H ,t = QH + Q K∑ k=0 B ( k; Qm ) RVt−k + Qmt (24) The last Regression (25) involves “realized power” defined as ∑m j=1 r 2 j ,t , which has been suggested by Barndorff-Nielsen and Shephard (2003) and Barndorff-Nelsen et al. (2004).”