Kevin Sheppard

28 papers receiving 2.0k citations

Hit Papers

Good Volatility, Bad Volatility: Signed Jumps and The Per...201520262018202220152015100200300400500

Peers

Kevin Sheppard
Comparison fields: 5 of 75
  • Finance 1.6k
  • Economics and Econometrics 1.6k
  • General Economics, Econometrics and Finance 606
  • Management Science and Operations Research 243
  • Statistics and Probability 84
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Countries citing papers authored by Kevin Sheppard

Since Specialization
Citations

This map shows the geographic impact of Kevin Sheppard'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 Kevin Sheppard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Sheppard more than expected).

Fields of papers citing papers by Kevin Sheppard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Sheppard. 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 Kevin Sheppard. The network helps show where Kevin Sheppard may publish in the future.

Co-authorship network of co-authors of Kevin Sheppard

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Sheppard. A scholar is included among the top collaborators of Kevin Sheppard 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 Kevin Sheppard. Kevin Sheppard 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
#WorkIndexed citations
1 4
2 52
3 84
4 30
5 39
6 2
7
Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatilitybreakdown →
519
8 7
9
Introduction to Python for Econometrics, Statistics and Data Analysis
3
10
Efficient and feasible inference for the components of financial variation using blocked multipower variation
7
11 3
12 76
13 15
14 37
15 113
16
MFE MATLAB Function Reference Financial Econometrics
11
17
Positive Semi-Definite Matrix Multiplicative Error Models
1
18
Evaluating the Specification of Covariance Models for Large Portfolios
41
19
Fitting and testing vast dimensional time-varying covariance models
34
20 2

About Kevin Sheppard

Kevin Sheppard is a scholar working on Finance, General Economics, Econometrics and Finance and Economics and Econometrics, having authored 28 papers that have together received 2.1k indexed citations. Recurring topics across this work include Financial Risk and Volatility Modeling (19 papers), Monetary Policy and Economic Impact (11 papers) and Market Dynamics and Volatility (8 papers). The work is most often cited by research in Finance (1.6k citations), General Economics, Econometrics and Finance (606 citations) and Economics and Econometrics (1.6k citations). Kevin Sheppard has collaborated with scholars based in United Kingdom, United States and France. Frequent co-authors include Andrew J. Patton, Neil Shephard, Lily Y. Liu, Robert F. Engle, Lorenzo Cappiello, Robert Engle, Fabrice Collard, Jean‐Marc Tallon, Sujoy Mukerji and Asger Lunde. Their work appears in journals such as The Review of Economics and Statistics, Journal of Econometrics and Journal of Business and Economic Statistics.

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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