High-frequency Data Cleaning
High-Frequency Data Cleaning is a systematic methodological framework within financial econometrics that distinguishes itself by addressing the unique noise, irregularities, and structural limitations inherent in high-frequency financial time series data through specialized preprocessing techniques such as outlier filtration, tick-level synchronization, and microstructure noise reduction.
16
Publications
839
Citations
99
Authors
45
Institutions
Publications per year
2017–2026
10
Parents
16
How to Clean Enhanced TRACE Data
Jens Dick‐Nielsen · SSRN Electronic Journal · 2013 · 245 citations · Full text
Business, Accountancy, Finance +17
Now-casting inflation using high frequency data
Michèle Modugno · International Journal of Forecasting · 2013 · 68 citations
Computing, Mathematics, Cosmology +10
1–5 of 16
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45
De Nederlandsche Bank
Amsterdam, The Netherlands
2 h-index
2 publications
157 citations
European Central Bank
Frankfurt am Main, Germany
2 h-index
3 publications
171 citations
Hungarian National Bank
Budapest, Hungary
2 h-index
2 publications
157 citations
Bank of Korea
Seoul, South Korea
2 h-index
2 publications
157 citations
Bank of Mexico
Mexico City, Mexico
2 h-index
4 publications
314 citations

Bank of Italy
Rome, Italy
2 h-index
2 publications
157 citations

Central Bank of Brazil
Brasília, Brazil
2 h-index
3 publications
300 citations

Bank of England
London, United Kingdom
2 h-index
2 publications
157 citations
1–8 of 45
Repository · 3 publications · 292 citations
3
h-index
1 publications · 24 citations
1
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ApJ · 1 publications · 56 citations
1
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1 publications · 35 citations
1
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1 publications · 33 citations
1
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1–5