Limit theorems for multipower variation in the presence of jumps

By Ole E. Barndorff-Nielsen, N. Shephard, Matthias Winkel. 2006

230 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/709067dd249fd6dd43c5abddbf42f4cc1df25780

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Citations in the corpus

Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (CARMA) Stochastic Volatility Models (2006, background): “Barndorff-Nielsen et al. (2006) contains sufficient conditions under which the presence of jumps in the price does not affect the consistency and asymptotic normality of the bipower variation.”
Asymptotic properties of realized power variations and related functionals of semimartingales (2006, methodology): “The case where X is discontinuous has been studied by Mancini [11], [12] (using processes similar to V ′′n(̟,α)) and Woerner [14], [15] (for the power and bi-power variations) and recently by Barndorff-Nielsen, Shephard and Winkel [5], and in those papers special cases of the forthcoming results may”
Asymptotic properties of power variations of Lévy processes (2005, result): “Let us quote for example [2–4], and some partial results when the process under considerations has jumps may be found in [5, 12] or [13], see also [1] in a different context.”
Outlyingness weighted covariation (2011, unknown): “By construction, this estimator of the ICov is asymptotically robust to finite activity jumps and in some cases also to infinite activity jumps (Barndorff-Nielsen et al., 2006).”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, background): “…Lévy processes; see, e.g., Cont and Tankov (2004) for a discussion of such processes, Todorov (2009) for an application involving jump-driven stochastic volatility models, and Barndorff-Nielsen et al. (2006) on using realized variation measures for certain infinite activity jump processes.”
Limit Theorems for Functionals of Higher Order Differences of Brownian Semi-Stationary Processes (2009, unknown): “[6], [8], [9], [10], [14], [16], [18], [23] for theory and applications.”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “Barndorff-Nielsen, Shephard, and Winkel (2004) showed that δ−1/2 ( {Yδ}[2/3,2/3,2/3]t − [Y ct]t ) has a mixed Gaussian limit and is robust to jumps.”
Detecting Jumps from Levy Jump Diffusion Processes (2010, methodology): “Li, Wells, and Yu (2006) use a Bayesian approach. there are Lévy jumps in continuous-time asset pricing models [see Aı̈t-Sahalia and Jacod (2008b, 2009, 2008a), Barndorff-Nielsen, Shephard, and Winkel (2006), Mancini (2006), and Todorov and Tauchen (2008)].”
Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, background): “BPVt consistently estimates the continuous sample path of quadratic variation as M → ∞ (Barndorff-Nielsen and Shephard, 2004, 2006; Barndorff-Nielsen et al., 2006):”
Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps (2007, unknown): “Such studies can be found in Barndorff-Nielsen, Shephard and Winkel [10], Woerner [23] and in a recent paper of Ait-Sahalia and Jacod [1] for the noiseless framework.”
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, background): “…by Barndorff-Nielsen and Shephard (2006) and it is defined as: MPVδ(X) [γ1,...,γM ] t = δ 1− 1 2 (γ1+...+γM ) [T/δ]∑ j=M M∏ k=1 |∆j−k+1X|γk (2.6) It is a natural extension of the concept of bipower variation, studied in Barndorff-Nielsen and Shephard (2004); Barndorff-Nielsen et al. (2006).”
Realised quantile-based estimation of the integrated variance (2010, background): “Exploiting the results of Section 8 (Part 2) in Barndorff-Nielsen et al. (2006) (recall that m is a fixed number), we find that, under condition (V), the convergence√ m n ( n∑ i=1 E [ ξ ni | F i−1n ] − ∫ 1 0 σ 2u du ) p → 0, (46) holds.”
Realized Jumps on Financial Markets and Predicting Credit Spreads (2006, background): “It should be pointed out that bipower variation also works for the infinite activity jumps (Barndorff-Nielsen et al., 2006; Jacod, in press), althoughwe focus solely on the case of rare and large jumps.”
Variance Risk-Premium Dynamics: The Role of Jumps (2010, unknown): “1 Calculation of P (IVa(t)|Gt) Combining central limit theorems in Barndorff-Nielsen et al. (2006) and Jacod (2008) about TVδ(t) and RVδ(t) respectively (in the presence of jumps) we have the following asymptotic approximation for TVδ(t) and RVδ(t) TVδ(t) = (1− π)θ + (IV (t)− (1− π)θ) + ν1t JVδ(t) =”
Jumps and Information Flow in Financial Markets (2012, background): “Shephard (2004), Barndorfi-Nielsen, Shephard, and Winkel (2005) , and A˜‡t-Sahalia and Jacod”
Multipower Variation for Brownian Semistationary Processes (2011, background): “Multipower variation The concept of (realised) multipower variation was originally introduced in [13] in the context of semimartingales, and the mathematical theory has been studied in a number of papers [6, 17, 24, 27] while various applications are the main subjects in [14–16, 25, 33].”
Efficient estimation of integrated volatility in presence of infinite variation jumps (2014, background): “[2, 3, 8]), which we do not explicitly recall here.”
Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence (2013, methodology): “In a noise-free framework, a well-known approach to obtain jump-robust estimators of (a functional of) volatility is the multipower variation (see, e.g., Barndorff-Nielsen, Shephard, and Winkel 2006).”