Mark Podolskij

3.3k total citations
73 papers, 1.7k citations indexed

About

Mark Podolskij is a scholar working on Finance, Statistics and Probability and Mathematical Physics. According to data from OpenAlex, Mark Podolskij has authored 73 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Finance, 19 papers in Statistics and Probability and 13 papers in Mathematical Physics. Recurrent topics in Mark Podolskij's work include Stochastic processes and financial applications (55 papers), Financial Risk and Volatility Modeling (47 papers) and Statistical Methods and Inference (16 papers). Mark Podolskij is often cited by papers focused on Stochastic processes and financial applications (55 papers), Financial Risk and Volatility Modeling (47 papers) and Statistical Methods and Inference (16 papers). Mark Podolskij collaborates with scholars based in Denmark, Germany and Luxembourg. Mark Podolskij's co-authors include Mathias Vetter, Kim Christensen, Jean Jacod, Per A. Mykland, Yingying Li, José Manuel Corcuera, Ole E. Barndorff–Nielsen, Silja Kinnebrock, Nikolaus Hautsch and Holger Dette and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Econometrics and The Annals of Statistics.

In The Last Decade

Mark Podolskij

69 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mark Podolskij Denmark 21 1.6k 837 291 285 114 73 1.7k
Cătălin Stărică United States 15 918 0.6× 649 0.8× 250 0.9× 272 1.0× 67 0.6× 33 1.1k
Marine Carrasco Canada 17 706 0.5× 517 0.6× 424 1.5× 495 1.7× 39 0.3× 39 1.2k
Mathias Vetter Germany 17 1.0k 0.7× 567 0.7× 252 0.9× 205 0.7× 25 0.2× 40 1.2k
Federico M. Bandi United States 19 1.8k 1.1× 1.2k 1.4× 198 0.7× 434 1.5× 22 0.2× 55 1.9k
Remigijus Leipus Lithuania 20 1.1k 0.7× 769 0.9× 350 1.2× 251 0.9× 168 1.5× 89 1.5k
Viktor Todorov United States 28 3.1k 2.0× 1.7k 2.0× 187 0.6× 476 1.7× 31 0.3× 96 3.4k
Joann Jasiak Canada 16 984 0.6× 688 0.8× 228 0.8× 361 1.3× 20 0.2× 48 1.3k
Holger Drees Germany 14 950 0.6× 383 0.5× 434 1.5× 228 0.8× 28 0.2× 37 1.1k
Paolo Zaffaroni United Kingdom 20 708 0.5× 788 0.9× 136 0.5× 505 1.8× 32 0.3× 49 1.2k

Countries citing papers authored by Mark Podolskij

Since Specialization
Citations

This map shows the geographic impact of Mark Podolskij's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark Podolskij with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Podolskij more than expected).

Fields of papers citing papers by Mark Podolskij

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Podolskij. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Podolskij. The network helps show where Mark Podolskij may publish in the future.

Co-authorship network of co-authors of Mark Podolskij

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Podolskij. A scholar is included among the top collaborators of Mark Podolskij based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark Podolskij. Mark Podolskij is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Christensen, Kim & Mark Podolskij. (2026). Realized range-based estimation of integrated variance. SSRN Electronic Journal.
2.
Podolskij, Mark, et al.. (2024). Optimal estimation of the local time and the occupation time measure for an α-stable Lévy process. SHILAP Revista de lepidopterología. 149–168.
3.
Podolskij, Mark, et al.. (2023). Estimation of mixed fractional stable processes using high-frequency data. The Annals of Statistics. 51(5). 2 indexed citations
4.
Podolskij, Mark, et al.. (2022). Multidimensional parameter estimation of heavy-tailed moving averages. Open Repository and Bibliography (University of Luxembourg). 2 indexed citations
5.
Podolskij, Mark, et al.. (2021). On estimation of quadratic variation for multivariate pure jump semimartingales. Open Repository and Bibliography (University of Luxembourg). 3 indexed citations
6.
Basse-O’Connor, Andreas, et al.. (2019). On limit theory for functionals of stationary increments Lévy driven moving averages. Electronic Journal of Probability. 24(none). 6 indexed citations
7.
Podolskij, Mark, et al.. (2017). Edgeworth expansion for the pre-averaging estimator. Stochastic Processes and their Applications. 127(11). 3558–3595. 4 indexed citations
8.
Podolskij, Mark, et al.. (2017). Estimation of the global regularity of a multifractional Brownian motion. Electronic Journal of Statistics. 11(1). 3 indexed citations
9.
Barndorff–Nielsen, Ole E., et al.. (2016). The Fascination of Probability, Statistics and their Applications : In Honour of Ole E. Barndorff-Nielsen. Springer eBooks. 3 indexed citations
10.
Podolskij, Mark, et al.. (2014). On non-standard limits of Brownian semi-stationary processes. Stochastic Processes and their Applications. 125(2). 653–677. 3 indexed citations
11.
Podolskij, Mark, et al.. (2014). High-frequency asymptotics for path-dependent functionals of Itô semimartingales. Stochastic Processes and their Applications. 125(4). 1195–1217. 1 indexed citations
12.
Christensen, Kim, Mark Podolskij, & Mathias Vetter. (2013). On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes. Journal of Multivariate Analysis. 120. 59–84. 43 indexed citations
13.
Corcuera, José Manuel, et al.. (2013). Asymptotic theory for Brownian semi-stationary processes with application to turbulence. Stochastic Processes and their Applications. 123(7). 2552–2574. 28 indexed citations
14.
Podolskij, Mark, et al.. (2012). Goodness-of-fit testing for fractional diffusions. RePEc: Research Papers in Economics. 4 indexed citations
15.
Christensen, Kim, Roel C. A. Oomen, & Mark Podolskij. (2011). Fact or Friction: Jumps at Ultra High Frequency. SSRN Electronic Journal. 46 indexed citations
16.
Podolskij, Mark & Mathias Vetter. (2009). Bipower-type estimation in a noisy diffusion setting. Stochastic Processes and their Applications. 119(9). 2803–2831. 82 indexed citations
17.
Barndorff–Nielsen, Ole E., José Manuel Corcuera, & Mark Podolskij. (2009). Limit Theorems for Functionals of Higher Order Differences of Brownian Semi-Stationary Processes. SSRN Electronic Journal. 4 indexed citations
18.
Jacod, Jean, Yingying Li, Per A. Mykland, Mark Podolskij, & Mathias Vetter. (2008). Microstructure noise in the continuous case: The pre-averaging approach. Stochastic Processes and their Applications. 119(7). 2249–2276. 449 indexed citations
19.
Barndorff–Nielsen, Ole E., José Manuel Corcuera, & Mark Podolskij. (2008). Power variation for Gaussian processes with stationary increments. Stochastic Processes and their Applications. 119(6). 1845–1865. 39 indexed citations
20.
Kinnebrock, Silja & Mark Podolskij. (2007). A note on the central limit theorem for bipower variation of general functions. Stochastic Processes and their Applications. 118(6). 1056–1070. 42 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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