Journals / Ege Akademik Bakış Dergisi / 2026 / Cilt: 24 Sayı: 2

Bibliometric Analysis of Studies of Published in the Field of Volatility

Özkan Şahin(DÜZCE ÜNİVERSİTESİ, İŞLETME FAKÜLTESİ)0000-0001-5341-1274
Erkan Bil(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, SİYASAL BİLGİLER FAKÜLTESİ)0000-0003-4301-3816
Pages
131–148
Publication Date
2024-05-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been observed that the main objectives of the studies conducted are either to compare the performance of existing volatility estimation models or to develop new models by adding new variables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Volatility, which is used to determine the risk structure of financial markets or instruments, is one of the most used methods by researchers.
📊
Key Findings & Data:In this study which is conducted to reveal the tag of volatility studies, 11,894 articles related to volatility between 1975 and 2020, which were scanned in the Web of Science database, were subjected to bibliometric analysis.
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Conclusion & Impact:It has been observed that the main objectives of the studies conducted are either to compare the performance of existing volatility estimation models or to develop new models by adding new variables.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Bibliometric Analysis · Volatility · Web of Science

Abstract

Volatility, which is used to determine the risk structure of financial markets or instruments, is one of the most used methods by researchers. In this study which is conducted to reveal the tag of volatility studies, 11,894 articles related to volatility between 1975 and 2020, which were scanned in the Web of Science database, were subjected to bibliometric analysis. As a consequence of the investigation, the details of the subject such as the country with the highest number of publications, institutions, the most cited authors, articles, leading journals and keywords in the field were revealed. As a consequence of the, it was determined that the authors worked on three basic subjects such as "stocks and stock markets", "exchange rates" and "macroeconomic indicators" on volatility. It has been observed that the main objectives of the studies conducted are either to compare the performance of existing volatility estimation models or to develop new models by adding new variables.

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Cite This Article

Şahin, Ö., Bil, E. (2024). Bibliometric Analysis of Studies of Published in the Field of Volatility. Ege Akademik Bakış Dergisi, Cilt: 24 Sayı: 2, 131–148. https://doi.org/10.21121/eab.922952

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References (29)

  1. Akel, V. (2011), Kriz Dönemlerinde Finansal Piyasalar Arasındaki Volatilite Yayılma Etkisi, 1. Basım, Detay Yayıncılık, Ankara
  2. Andersen, Torben, G; Bollerslev, T; Diebold, FX; Labys, P. (2003). Modeling and Forecasting Realized Volatility, Econometrica, 71 (2); 579-625
  3. Andersen, Torben, G.; Bollerslev, Tim; Diebold, Francis X., (2007). Roughing It Up: Including Jump Components in The Measurement, Modeling, and Forecasting of Return Volatility, Review of Economics and Statistics, 89 (4); 701-720
  4. Andersen, TG; Bollerslev, T; Diebold, FX; Labys, P, The Distribution of Realized Exchange Rate Volatility, Journal of American Statistical Association, 2001, 96(453);42-55
  5. Andersen, TG; Bollerslev, T., Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts, International Economic Review, 1998, 39(4);885-905
  6. Bekaert, G; Wu, GJ, Asymmetric Volatility and Risk in Equity Markets, Review of Financial Studies, 2000, 13(1);1-42
  7. Baker, H. K., Kumar, S., & Pandey, N. (2019). Thirty years of the Global Finance Journal: A bibliometric analysis. Global Finance Journal, 100492.
  8. Barndorff-Nielsen, OE; Shephard, N., Econometric Analysis of Realized Volatility and Its use in Estimating Stochastic Volatility Models, Journal of The Royal Ststistical Society Series B-Statistical Methodology, 2002, 64;253-280
  9. Bollerslev, T. Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, Volume 31, Issue 3, 1986, Pages 307-327
  10. Chen, J., & Yang, L. (2019). A Bibliometric Review of Volatility Spillovers in Financial Markets: Knowledge Bases and Research Fronts. Emerging Markets Finance and Trade, 1-22.
  11. Depren, Ö., Kartal, M. T., & Depren, S. K. (2018). Borsalarda Oynaklık Üzerine Yayınlanmış Akademik Çalışmaların Bibliyometrik Analizi. Bankacılık ve Sermaye Piyasası Araştırmaları Dergisi, 2(6), 1-15.
  12. Diebold, FX; Nerlove, M., The Dynamics of Eschange-Rate-Volatility-Amultivariate Latent Factor ARCH Model, Journal of Applied Econometrics, 1989, 4(1);1-21
  13. Emhan, Abdürrahim, (2009). “Risk Yönetim Süreci ve Risk Yönetmekte Kullanılan Teknikler”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23 (3), 209 -220
  14. Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation Econometrica 50: 987-1008.
  15. Flores‐Sosa, M., Avilés‐Ochoa, E., & Merigó, J. M. (2020). Exchange rate and volatility: A bibliometric review. International Journal of Finance & Economics.
  16. French, KR; Schwert, GW; Stambaugh, RF, Expected Stock Returns and Volatility, Journal of Financial Economics, 1987, 19(1);3-29
  17. Glosten, LR; Jagannathan, R; Runkle, DE, On The Relation Between The Expected Value and The Volatility of The Nominal Excess Return on Stocks, Journal of Finance, 1993, 48(5);1779-1801
  18. Heston, SL (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies, 6(2), 327-343
  19. Hussain, A., Fatima, N. ve Kumar, D. (2011). Bibliometric analysis of the 'Electronic Library' journal (2000-2010). Webology, 8(1): 1-11.
  20. Kabongo, J. (2019). Twenty Years of the Journal of African Business: A Bibliometric Analysis, Journal of African Business, 20(2), 170-179
  21. Merediz-Solà, I., & Bariviera, A. F. (2019). A bibliometric analysis of bitcoin scientific production. Research in International Business and Finance, 50, 294-305.
  22. Merigó, J. M. and Yang, J. B. (2016), Accounting Research: A Bibliometric Analysis, Australian Account Review, 27(1), 71-100
  23. Patton, Andrew J. Volatility Forecast Comparison Using Imperfect Volatility Proxies, Journal of Econometrics, 2011, 160(1);246-256
  24. Poon, SH; Granger, CWJ, Forecasting Volatility in Financial Markets: A Review, Journal of Economic Litarature, 2003, 41(2);478-539
  25. Schwert, GW, Stock Volatility and The Crash of 87, Review of Financial Studies, 1990, 3(1;77-106
  26. Schwert, GW, Why Does Stock-Market Volatility Change Over Time, Journal of Finance, 1989, 44(5), 1115-1153
  27. Vianez, J. P, Martínez, R. G. and Román, C, P. (2020), A bibliometric analysis of behavioural finance with mapping analysis tools, European Research on Management and Business Economics, 26(2), 71-77
  28. Wei, G. (2016), “A Bibliometric Analysis of The Top Five Economics Journals During 2012–2016.”, Journal of Economic Survey, 33(1), 25-59
  29. Wiggins, JB, Option Values Under Stochastic Volatility-Theory and Empirical Estimates, Journal of Financial Economics, 1987, 19(2);351-372