Nikolaus Hautsch

Finance · 2001–2016

A researcher in the Science corpus: 14 papers; trusts 59 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Finance — 194 citations into it; #1,999 of 10,518 by trust here, #1,689 by citations
• Economics and Econometrics — 153 citations into it; #5,465 of 23,481 by trust here, #7,096 by citations
• Statistics and Probability — 48 citations into it; #5,928 of 12,932 by trust here, #4,809 by citations
• Management Science and Operations Research — 23 citations into it; #5,081 of 11,946 by trust here, #8,906 by citations
• Accounting — 17 citations into it
• Artificial Intelligence — 10 citations into it; #27,747 of 35,345 by trust here, #23,077 by citations
• Global and Planetary Change — 8 citations into it; #5,684 of 10,225 by trust here, #8,787 by citations
• Computational Theory and Mathematics — 5 citations into it

Whom Nikolaus Hautsch trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ole E. Barndorff-Nielsen
2. Torben G. Andersen
3. P. R. Hansen
4. N. Shephard
5. Robert F. Engle
6. Tim Bollerslev
7. Asger Lunde
8. Fulvio Corsi
9. Lan Zhang
10. Jean Jacod

Who trusts Nikolaus Hautsch overall

1. Maria Pacurar
2. Luc Bauwens
3. Patrick J. Laub
4. Massimiliano Caporin
5. Wolfgang Karl Härdle
6. Asger Lunde
7. Markus Bibinger
8. Gangjin Wang
9. Thomas Taimre
10. Jean-François Richard

Papers from Nikolaus Hautsch's perspective

Ordered by how strongly the researchers Nikolaus Hautsch trusts endorse each paper. Same corpus, a different order for every researcher.

1. Separating Microstructure Noise from Volatility
2. Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics
3. Generalized autoregressive conditional heteroskedasticity
4. Econometric analysis of realized volatility and its use in estimating stochastic volatility models
5. Realized Variance and Market Microstructure Noise
6. MODELING AND FORECASTING REALIZED VOLATILITY
7. Dynamic Conditional Correlation
8. Power and bipower variation with stochastic volatility and jumps
9. The distribution of realized stock return volatility
10. The jump-risk premia implicit in options: evidence from an integrated time-series study $

Their papers

• Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency (2013) — 104 citations
• Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery (2007) — 138 citations
• A blocking and regularization approach to high dimensional realized covariance estimation (2012) — 123 citations
• Financial Network Systemic Risk Contributions (2015) — 516 citations
• Stochastic Conditional Intensity Processes (2006) — 107 citations
• Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence (2013) — 87 citations
• Volatility estimation on the basis of price intensities (2001) — 59 citations
• Econometrics of Financial High-Frequency Data (2011) — 185 citations
• Systemic risk spillovers in the European banking and sovereign network (2016) — 149 citations
• Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence (2014) — 57 citations
• Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013) — 88 citations
• Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011) — 61 citations
• Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes (2010) — 67 citations
• Modelling Irregularly Spaced Financial Data: Theory and Practice of Dynamic Duration Models (2004) — 73 citations