Institution profile

University of Amsterdam

Academic institutioneurope · nl
Official website
Research library1,157linked papers
Opportunities0open roles
Selected work

Representative Papers

Online Behavioral Advertising: A Literature Review and Research Agenda

Jun 29, 2017

Online behavioral advertising (OBA) suffers from conceptual ambiguity and fragmented empirical evidence, hindering theoretical advancement and practical guidance. Method: Through a systematic literature review and interdisciplinary theoretical integration, this study develops the first precise definition of OBA and a unified theoretical framework that jointly models advertiser-controllable variables (e.g., data transparency, personalization intensity) and consumer-level characteristics (e.g., privacy concern, digital literacy). Contribution/Results: The study maps the distribution of existing empirical findings and identifies three critical research gaps: (1) mechanistic black boxes, (2) contextual boundary conditions, and (3) cross-cultural variations. It proposes empirically testable future research directions and policy design principles that balance advertising efficacy with privacy protection. This work provides an integrative scholarly foundation for advancing OBA theory, optimizing precision advertising practices, and informing data governance policy.

354 citations33 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

Singling out people without knowing their names - Behavioural targeting, pseudonymous data, and the new Data Protection Regulation

Feb 16, 2016Computer Law and Security Review

This paper addresses the individual identifiability of pseudonymous data in behavioral targeting advertising, challenging whether such data qualifies as “personal data” under regulations like the GDPR. Methodologically, it integrates legal text analysis, privacy risk assessment frameworks, and empirical modeling using real-world behavioral datasets. Results demonstrate that non-identifying attributes—such as device identifiers and browsing trajectories—enable high-accuracy re-identification of natural persons, exposing a critical interpretive gap in the prevailing legal “single individual” definition vis-à-vis contemporary technical capabilities. The paper makes two key contributions: first, it proposes a novel two-dimensional criterion for personal data classification—grounded in *identifiability likelihood* and *identification cost*; second, it recommends regulatory adaptation requiring platforms to bear the burden of proof for the ongoing effectiveness of anonymization measures. These findings provide both theoretical grounding and actionable policy pathways for enhancing the technical enforceability of data protection law.

67 citations7 influentialRead paper

Tight Bounds for Quantum Phase Estimation and Related Problems

May 08, 2023Embedded Systems and Applications

This work establishes tight query complexity bounds—up to logarithmic factors—for quantum phase estimation (QPE) and its variants across all parameter regimes. We consider three problems: standard QPE, QPE with a prior auxiliary state overlapping the target eigenspace by at least γ, and maximum eigenphase estimation. Using techniques including trigonometric polynomial analysis, information-theoretic lower bound derivation, constructive algorithm design, and error amplification, we prove that achieving precision δ with failure probability ε requires Ω((1/δ) log(1/ε)) queries—matching the best-known upper bounds. Our results precisely quantify the utility of auxiliary states and prior knowledge, revealing fundamental limits on their effectiveness. Moreover, we fully resolve the query complexity of the Unitary recurrence time problem.

18 citations4 influentialRead paper
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