Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation
By Ole E. Barndorff-Nielsen, N. Shephard. 2005
1,379 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/607f5e7e17de8859f97cce45c14bb2043ce84964
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Tim Bollerslev
- V. Todorov
- Andrew J. Patton
- N. Shephard
- Kevin Sheppard
- Asger Lunde
- Mark Podolskij
- P. R. Hansen
In Finance
- Tim Bollerslev
- V. Todorov
- Andrew J. Patton
- N. Shephard
- Mark Podolskij
- Kevin Sheppard
- Ole E. Barndorff-Nielsen
- Asger Lunde
Citations in the corpus
- Measuring the impact of jumps in multivariate price processes using bipower covariation (2004, neutral): “Huang and Tauchen (2003) have reported extensive Monte Carlo experiments on the performance of the central limit theory for realised BPV developed by Barndorff-Nielsen and Shephard (2003).”
- Outlyingness weighted covariation (2011, result): “609 (Barndorff-Nielsen and Shephard, 2006).”
- 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): lim M→∞ BPVt ≡ t∫ t−1 σ2sds (7) where all variables are as previously defined.”
- Testing for Common Arrivals of Jumps for Discretely Observed Multidimensional Processes (2009, methodology): “Tests for deciding whether a given path has jumps or not on the interval [0, T ] have been already developed (see [1, 3, 4, 12–15, 17] and [5] discuss some multivariate extensions of the test in [4]).”
- Fact or friction: jumps at ultra high frequency (2014, neutral): “In an influential series of papers, Barndorff-Nielsen and Shephard (2004, 2006) and Barndorff-Nielsen, Shephard, and Winkel (2006) introduce the concept of (bi-) power variation—a simple but effective technique to identify and measure the variation of jumps from intraday data [see A¨ıt-Sahalia and…”
- The Volatility of Realized Volatility (2005, methodology): “Based on the realized quarticity measure, Barndorff-Nielsen and Shephard (2006) show in a simulation study that the implied standard errors for the logarithmic transformation indeed tend to be smaller than those of the realized variance statistic given in (7), with integrated variance being also e”
- Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance (2012, methodology): “A common test in the literature, and one that exemplifies the usefulness of RJ BV,t in statistical inference, is proposed by Barndorff-Nielsen and Shephard (2005).”
- Understanding limit theorems for semimartingales: a short survey (2009, methodology): “…and realised jumps (see, e.g. Theorem 2, Example 3 and Theorem 4 of this paper or Barndorff-Nielsen and Shephard (2004), Mancini (2001)), or for performing various test procedures (see, e.g. Aït-Sahalia and Jacod, 2009; Barndorff-Nielsen and Shephard, 2006; Dette, Podolskij and Vetter (2006)).”
- Forecasting Volatility Using High-Frequency Data (2011, background): “These authors estimated the jump component as the difference between the realized variance and the the bipower variation, see Barndorff-Nielsen and Shephard (2004, 2006).”
- VAST VOLATILITY MATRIX ESTIMATION FOR HIGH-FREQUENCY FINANCIAL DATA (2010, background): “realized variation (BPRV) [ Barndorff-Nielsen and Shephard (2006) ], two-time scale real-”