Dimos Kambouroudis

405 total citations
15 papers, 253 citations indexed

About

Dimos Kambouroudis is a scholar working on Finance, Economics and Econometrics and General Economics, Econometrics and Finance. According to data from OpenAlex, Dimos Kambouroudis has authored 15 papers receiving a total of 253 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Finance, 13 papers in Economics and Econometrics and 7 papers in General Economics, Econometrics and Finance. Recurrent topics in Dimos Kambouroudis's work include Market Dynamics and Volatility (13 papers), Financial Risk and Volatility Modeling (12 papers) and Monetary Policy and Economic Impact (7 papers). Dimos Kambouroudis is often cited by papers focused on Market Dynamics and Volatility (13 papers), Financial Risk and Volatility Modeling (12 papers) and Monetary Policy and Economic Impact (7 papers). Dimos Kambouroudis collaborates with scholars based in United Kingdom, Jordan and Cyprus. Dimos Kambouroudis's co-authors include David G. McMillan, Dimitris A. Tsouknidis, Konstantinos Gavriilidis, Abhinav Goyal, Vasileios Kallinterakis and Jason Laws and has published in prestigious journals such as Transportation Research Part E Logistics and Transportation Review, Finance research letters and International Review of Financial Analysis.

In The Last Decade

Dimos Kambouroudis

14 papers receiving 245 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dimos Kambouroudis United Kingdom 7 196 139 68 48 36 15 253
Panagiotis Tziogkidis United Kingdom 7 213 1.1× 122 0.9× 44 0.6× 95 2.0× 14 0.4× 11 304
Gregorio Serna Spain 9 281 1.4× 324 2.3× 91 1.3× 29 0.6× 18 0.5× 30 447
Michael Tamvakis United Kingdom 10 344 1.8× 105 0.8× 185 2.7× 25 0.5× 46 1.3× 18 420
Mo Yang China 10 243 1.2× 59 0.4× 69 1.0× 21 0.4× 11 0.3× 39 331
Moawia Alghalith Trinidad and Tobago 10 227 1.2× 172 1.2× 49 0.7× 72 1.5× 14 0.4× 85 374
Ruijun Bu United Kingdom 9 205 1.0× 166 1.2× 59 0.9× 37 0.8× 4 0.1× 24 321
Sangmok Kang South Korea 5 300 1.5× 124 0.9× 132 1.9× 54 1.1× 6 0.2× 25 324
Fred Joutz United States 5 214 1.1× 54 0.4× 191 2.8× 46 1.0× 13 0.4× 7 264
Shaobo Wen China 11 378 1.9× 94 0.7× 84 1.2× 46 1.0× 10 0.3× 19 463

Countries citing papers authored by Dimos Kambouroudis

Since Specialization
Citations

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

Fields of papers citing papers by Dimos Kambouroudis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dimos Kambouroudis

This figure shows the co-authorship network connecting the top 25 collaborators of Dimos Kambouroudis. A scholar is included among the top collaborators of Dimos Kambouroudis 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 Dimos Kambouroudis. Dimos Kambouroudis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Kambouroudis, Dimos, et al.. (2025). Forecasting realised volatility using regime-switching models. International Review of Economics & Finance. 101. 104171–104171.
2.
McMillan, David G., et al.. (2024). Left-tail risk and UK stock return predictability: Underreaction, overreaction, and arbitrage difficulties. International Review of Financial Analysis. 95. 103333–103333. 1 indexed citations
3.
McMillan, David G., et al.. (2023). Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets. Journal of Economics and Finance. 47(3). 723–762. 6 indexed citations
4.
Kambouroudis, Dimos, et al.. (2023). Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets. Finance research letters. 55. 103992–103992. 5 indexed citations
5.
McMillan, David G., et al.. (2022). Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets. Empirical Economics. 64(4). 1517–1537. 10 indexed citations
6.
McMillan, David G., et al.. (2022). Expected profitability, the 52-week high and the idiosyncratic volatility puzzle. European Journal of Finance. 29(14). 1621–1648. 3 indexed citations
7.
Kambouroudis, Dimos, et al.. (2021). Do Artificial Neural Networks Provide Improved Volatility Forecasts: Evidence from Asian Markets. SSRN Electronic Journal. 2 indexed citations
8.
Kambouroudis, Dimos, et al.. (2021). Forecasting realised volatility: Does the LASSO approach outperform HAR?. Journal of International Financial Markets Institutions and Money. 74. 101386–101386. 12 indexed citations
9.
Kambouroudis, Dimos, et al.. (2021). Forecasting Realised Volatility: Does the LASSO approach outperform HAR?. SSRN Electronic Journal. 1 indexed citations
10.
Goyal, Abhinav, Vasileios Kallinterakis, Dimos Kambouroudis, & Jason Laws. (2018). Cross-border exchanges and volatility forecasting. Quantitative Finance. 18(5). 789–799. 1 indexed citations
11.
Gavriilidis, Konstantinos, et al.. (2018). Volatility forecasting across tanker freight rates: The role of oil price shocks. Transportation Research Part E Logistics and Transportation Review. 118. 376–391. 72 indexed citations
12.
Kambouroudis, Dimos, et al.. (2016). Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models. Journal of Futures Markets. 36(12). 1127–1163. 57 indexed citations
13.
Kambouroudis, Dimos & David G. McMillan. (2015). Is there an ideal in-sample length for forecasting volatility?. Journal of International Financial Markets Institutions and Money. 37. 114–137. 6 indexed citations
14.
Kambouroudis, Dimos & David G. McMillan. (2015). Does VIX or volume improve GARCH volatility forecasts?. Applied Economics. 48(13). 1210–1228. 38 indexed citations
15.
McMillan, David G. & Dimos Kambouroudis. (2009). Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. International Review of Financial Analysis. 18(3). 117–124. 39 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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