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Aarhus University

Academic institutioneurope · dk
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Research library496linked papers
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Selected work

Representative Papers

Realized Range-Based Estimation of Integrated Variance

Jun 04, 2006

This study addresses the downward bias and inefficiency of conventional realized variance estimators under discrete observations by proposing a novel high-low range–based estimator for the quadratic variation of continuous semimartingales. The method replaces squared returns with normalized squared price ranges, yielding a consistent and asymptotically mixed normal estimator that effectively corrects bias induced by non-trading periods. Leveraging probabilistic limit theory, continuous semimartingale modeling, and high-low price statistics, the approach achieves an 80% reduction in theoretical variance compared to traditional estimators. Empirical analysis using TAQ data demonstrates that the proposed estimator substantially outperforms existing benchmarks in terms of estimation accuracy, efficiency, and robustness.

251 citations42 influentialRead paper

Fact or Friction: Jumps at Ultra High Frequency

Jan 31, 2014

This study addresses the well-documented bias in conventional jump detection methods, which, when applied to low-frequency data, systematically overestimate price jumps by misattributing high-frequency market microstructure noise to genuine discontinuities. Leveraging millisecond-level tick-by-tick transaction data, the authors propose a novel framework that integrates nonparametric jump detection with tick-level volatility decomposition to identify true price jumps at the order-book level. This approach effectively disentangles market microstructure noise from authentic jump signals. The findings reveal that the contribution of genuine jumps to price variation is an order of magnitude smaller than previously reported in the literature, suggesting that jumps are far rarer events than commonly assumed and thereby revising the prevailing understanding of jump dynamics in financial markets.

203 citations19 influentialRead paper

Realised quantile-based estimation of the integrated variance

Sep 15, 2010

This study addresses the challenges posed by jumps, outliers, and market microstructure noise in high-frequency financial data when estimating realized variance. The authors propose a robust quantile-based estimation method that constructs a quantile-type variance estimator asymptotically immune to finite-activity jumps and outliers, and extend it to noisy high-dimensional settings. Theoretical analysis demonstrates that the proposed estimator consistently recovers the integrated variance at the optimal convergence rate and exhibits favorable asymptotic efficiency. Monte Carlo simulations confirm its pronounced robustness in finite samples, and empirical applications to equity data further validate the practical effectiveness of the approach.

131 citations12 influentialRead paper

The Drift Burst Hypothesis

Sep 27, 2016Journal of Econometrics

This study investigates the existence, market prevalence, and underlying mechanisms of transient, localized “drift bursts” in financial asset prices. To this end, we incorporate drift bursts into a continuous-time Itô semimartingale framework and develop a theoretical model under no-arbitrage conditions, alongside a nonparametric test statistic designed to reliably detect such events from high-frequency data contaminated by noise. Our work is the first to formally model drift bursts as a regular feature of financial markets, uncovering their intrinsic links to liquidity shocks and price reversals. Empirical analysis reveals that drift bursts are pervasive across equity, bond, foreign exchange, and commodity markets, occurring on average once per week; notably, negative bursts accompanied by high trading volume are more likely to trigger significant price reversals.

64 citations10 influentialRead paper

Is the Diurnal Pattern Sufficient to Explain Intraday Variation In Volatility? A Nonparametric Assessment

Aug 01, 2016Journal of Econometrics

This study investigates whether intraday volatility dynamics are entirely driven by deterministic diurnal patterns. To this end, the authors propose a nonparametric approach that extends pre-averaged bipower variation to high-frequency data featuring jumps and market microstructure noise within a general Itô semimartingale framework, yielding a robust estimator of the diurnal scaling factor. A test statistic is constructed based on seasonally adjusted returns, and an improved bootstrap procedure is introduced to enhance finite-sample inference. Empirical results show that while the diurnal pattern accounts for a substantial portion of intraday volatility, significant residual heteroskedasticity remains, indicating the presence of additional time-varying sources of volatility beyond the deterministic seasonal component.

50 citations7 influentialRead paper
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