Impact of jumps on returns and realised variances: econometric analysis of time-deformed Lévy processes

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

95 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f0dbb70389e648ee61a32884d24e8fcb472c072f

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Citations in the corpus

Econometric analysis of jump-driven stochastic volatility models (2011, methodology): “Barndorff-Nielsen and Shephard (2002, 2006) have used the realized variance to estimate via QMLE part of the parameters of Non-Gaussian OU-SV models and time-changed Lévy models5.”
Power and bipower variation with stochastic volatility and jumps (2003, methodology): “See Barndorff-Nielsen and Shephard (2003d).”
Stochastic Volatility: Origins and Overview (2008, background): “Empirical evidence in Barndorff-Nielsen and Shephard (2006) suggest these”
Multipower Variation for Brownian Semistationary Processes (2011, background): “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].”
Integer-valued Lévy processes and low latency financial econometrics (2012, background): “These include Barndorff-Nielsen and Shephard ( 2006 ) and Bacry et al.”
Bipower Variation for Gaussian Processes with Stationary Increments (2009, unknown): “The theory of bipower, and more generally multipower, variation has been developed out of questions raised in mathematical finance; for motivation, some first results, and applications, see [2], [3], [4], [5], [6], [8], [29], [30], [31], and [32].”
Volatility estimators for discretely sampled Lévy processes (2007, background): “Wiener process, Y is typically a compound Poisson or other specific Levy process and σ may be stochastic (see, e.g., [ 4 , 15, 21, 22]).”
Stochastic volatility with leverage: Fast and efficient likelihood inference (2007, background): “For example, Chib et al. (2002) extended the basic KSC approach by letting yt 1⁄4 ffiffiffiffi lt p t expðht=2Þ, (19) htþ1 1⁄4 mþ fðht mÞ þ Zt; t 1⁄4 0; 1; .”
INFERENCE FOR THE JUMP PART OF QUADRATIC VARIATION OF ITÔ SEMIMARTINGALES (2009, methodology): “Besides, one can use ther alised bipower variation(as defined by Barndorff-Nielsen & Shephard (2004, 2006)) for estimating the continuous part of the quadratic variation of Itô semimartingales (see Jacod (2006)).”
Estimation of continuous-time stochastic volatility models with jumps using high-frequency data (2009, methodology): “…tests have been proposed for the presence of jumps on the “observed” path of increasingly finer-sampled processes, see Ait-Sahalia and Jacod (2008b), Barndorff-Nielsen and Shephard (2004, 2006a), Huang and Tauchen (2005), Jiang and Oomen (2005), Lee and Mykland (2007), Mancini (2001).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “Closer to the content of this paper, Barndorff-Nielsen and Shephard (2005b) study the first two moments of realised QV when Yt = Zτ t ,where τ is the integral of a non-negative covariance stationary process, that is a process with non-decreasing sample paths, and Z is a Lévy process.”
Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (CARMA) Stochastic Volatility Models (2006, methodology): “This contrast suggests that the simulation methods developed in this article could be used for a far more extended analysis of the properties of the jump-detection tests of Barndorff-Nielsen and Shephard (2006a) than that conducted by Huang and Tauchen (2006).”