CURRICULUM VITAE

1. Name & Surname : Özgür Ömer Ersin

2. Academic Title : Assoc.Prof.Dr.

3. Degrees :

Degree Research Area University Year

Undergraduate Economics (in English) Eastern Mediterranean University (Honors degree due to the CGPA record)

2001

Masters Economics Yıldız Technical University 2005

Ph.D Economics Yıldız Technical University 2009

4. Academic Titles

Teaching Assistant

International Trade and Business (in English)

Yeditepe University 2007-2009

Assist.Prof.Dr. İngilizce İktisat Beykent University 2009-2013

Assoc.Prof.Dr. Macroeconomics /Development Economics

ÜAK (Üniversiteler Arası Kurul), Sept, 2013. Appointment: Beykent University, Faculty of Business and Administrative Sciences, Dept.of Economics (EN), Oct, 2013.

2013-

Head of Dept. Dept. of Economics (English)

Beykent University 2015-

Title of the PhD Thesis: “ANALYSIS OF FISCAL THEORY OF PRICE LEVEL WITH NONLINEAR TIME SERIES MODELS IN TURKEY” (“Türkiye’de Fiyatlar Genel Düzeyine İlişkin Maliye Teorisinin Doğrusal Olmayan Zaman Serisi Modelleri Bakımından İncelenmesi”) Yıldız Technical University, Social Sciences Institute, Dept. of Economics, Economics Ph.D Programme, 2009, Istanbul.

5. Master’s and Ph. D Thesis Supervised

5.1. Master’s Thesis Supervised 5.2. Ph. D Thesis Supervised 6. Publications 6.1. Articles Published on International Refereed Journals

SSCI, SCI Indexed Articles

1. Bildirici, M., Parasız, İ., Ersin, Ö., Aykaç-Alp, E. (2015), “Psychological Dominance, Market Dominance and their Impacts in Turkey,” Economic Computation and Economic Cybernetics Studies and Research, Vol. 49, Issue 4, 2015, pp. 171-191. (SSCI ve SCI-E)

2. Bildirici, M., Ersin, Ö. (2015), "Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach," Petroleum Science, 2015 12 (3), pp. 534-552. (SCI-Expanded, SCOPUS)

3. Bildirici, M., Ersin, Ö. (2014), “Nonlinearity, Volatility and Fractional Integration in Daily Petrol Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models,” Romanian Journal of Economic Forecasting, 2014 Issue 3. (SSCI)

4. Bildirici, M., Ersin, Ö. (2014), “Asymmetric Power and Fractionally Integrated Support Vector and Neural Network GARCH Models with an Application to Forecasting Financial Returns in ISE100 Stock Index,” Economic Computation and Economic Cybernetics Studies and Research, 48 (2), 163-184. (SSCI,SCI-E)

5. Bildirici, M., Ersin, Ö. (2014), “Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns,” Scientific World Journal, 2014, Article No. 497941. (SCI, SCOPUS)

6.Bildirici, M., Ersin, Ö. (2013), “Forecasting Oil Prices: Smooth Transition and Neural Network Augmented

GARCH Family Models,” Journal of Petroleum Science and Engineering, 109, 230–240. (SCI)

7. Ersin, Ö. (2012), “Türkiye’de Reel Döviz Kuru Serisinin Doğrusal Olmayan Ekonometrik Modeller ile İncelenmesi: Band-TAR ve STAR Modelleri,” İktisat, İşletme ve Finans, Cilt 27, Sayı 319 (Ekim), 89-122. (SSCI). 8. Bildirici, M., Ö. Ersin, Kökdener, M. (2011), “Genetic Structure, Consanguineous Marriages and Economic Development: Panel Cointegration and Panel Cointegration Neural Network Analyses,” Expert Systems with Applications, Vol. 48, Issue 5, May 2011, pp. 6153-6163. (SCI).

9. Bildirici, M., Kökdener, M. Ersin, Ö. (2010), “An Empirical Analysis of the Effects of Consanguineous Marriages on Economic Development” Journal of Family History, Vol. 35, Issue 4. (SSCI).

10. Bildirici, M., Ö. Ersin, E. Alp, (2010), “TAR-Cointegration Neural Network Model: An Empirical Analysis of Exchange Rates and Stock Returns,” Expert Systems with Applications, Vol 37, Issue 1, Jan 2010, 2-12. (SCI).

11.Bildirici, M, Ö. Ersin (2009), “Improving Forecasts of GARCH Family Models with the Artificial Neural Networks: An Application to the Daily Returns in Istanbul Stock Exchange,” Expert Systems with Applications, Vol. 36, Issue 4, 7355-7362. (SCI).

Articles Indexed in other International Indices (Ebsco, Proquest, Econlit and others.)

12. Bildirici, M., Ersin, Ö., Türkmen, C., Yalcinkaya, Y. (2012), “The Persistence Effect of Unemployment in Turkey: An Analysis of the 1980-2010 Period,” Journal of Business, Economics and Finance Vol. 1, Issue 3, ISBN: 2146-7943, pp.22-32. (EBSCO, Index Copernicus)

13. Ersin, Ö. (2011), “Türkiye’de Mali Sürdürülebilirliğin Doğrusal Olmayan Bir Analizi: MLSTAR Çoklu Lojistik Yumuşak Geçişli Otoregresif Modeli,” Ege Akademik Bakış Dergisi, Cilt 11, pp. 41-58. (PROQUEST, EBSCO, ECONLIT)

14. Bildirici, M., Ersin, Ö. (2011), "Fiyat Teorisinin Mali Teorisine Farklı Bir Bakış: MLSTAR ve MLP Modelleri," TÜSİAD-Koç University Economic Research Forum WP 1115, TUSIAD-Koc University Economic Research Forum. (IDEAS-REPEC)

15. Bildirici, M., Ö. Ersin (2010), “The Role of Consanguineous Marriage on the Success of Asia and Failure of Africa: Panel Neural Network Analysis,” Asian-African Journal of Economics and Econometrics, Issue 1-2010, 191-208. (ECONLIT).

16. Bildirici, M, Ö. Ersin, E. Aykaç Alp (2008), “An Empirical Analysis of Debt Policies, External Dependence, Inflation and Crisis in the Ottoman Empire and Turkey: 1830-2005 Period,” Applied Econometrics and International Development, Vol. 8, No. 2. (ECONLIT).

17. Bildirici, M. Ö. Ersin (2008), “An Empirical Analysis of the Inflationary Effects of Costs of Domestic Debt under Active and Passive Fiscal Policy,” Yapi Kredi Economic Review 19 (1), 3-25. (ECONLIT).

18. Bildirici, M., Ö. Ersin (2007), “Domestic Debt, Inflation and Crises: A Panel Cointegration Application to Emerging and Developed Economies,” Applied Econometrics and International Development AEID, 2007-1. (ECONLIT).

19. Bildirici, M., Ö. Ö. Ersin (2005), “Fiscal Theory of Price Level and Economic Crises: The Case of Turkey” Journal of Social and Economic Research JESR, Vol 7(2), pp. 81-114 (ECONLIT).

The international proceeding articles indexed in Sciencedirect database(SDI) ISI and WOS

20. Ersin, Ö. (2016), “The nonlinear relationship of environmental degradation and income for the 1870-2011 period in selected developed countries: the dynamic Panel-STAR approach,” Procedia Economics and Finance Vol. 38, pp.318-339. (SDI)

21. Bildirici, M, Ersin, Ö., Kökdener, M. (2016), “An Investigation of Hemophilia, Consanguineous Marriages and Economic Growth: Panel MLP and Panel SVR Approach,” Procedia Economics and Finance Vol. 38, pp.294-307. (Science Direct Database)

22. Bildirici, M, Ersin, Ö. (2016), “Markov Switching Artificial Neural Networks for Modelling and Forecasting Volatility: An Application to Gold Market,” Procedia Economics and Finance Vol. 38, pp.106-121. (SDI)

23. Bildirici, M., Ersin,Ö. (2015), “An Investigation of the Relationship between the Biomass Energy Consumption, Economic Growth and Oil Prices,” Procedia-Social and Behavioral Sciences Vol. 210, pp. 203-212. (SDI)

6.2. Proceedings Presented and Published at International Conferences

1. Ersin, Ö. (2011), “Türkiye’de Mali Sürdürülebilirliğin Doğrusal Olmayan Bir Analizi: MLSTAR Çoklu Lojistik Yumuşak Geçişli Otoregresif Modeli,” 12th International Symposium on Econometrics Operations Research and Statistics, 26-29 May. 2011, Pamukkale Üniversitesi, Denizli, in the Proceedings Book: Econometrics, pp. 733-741.

2. Ersin, Ö. (2011), “Türkiye’de Reel Efektif Döviz Kuru Serilerinin Doğrusal Olmayan Ekonometrik Modeller ile İncelenmesi: BAND-TAR ve STAR Modelleri,” ICEF International Conference on Economics and Finance, 20-21 Mayıs 2011, Haliç Congress Center, Istanbul.

3.Ercilasun, M., Hiç Gencer, A., Ersin, Ö. (2011), “Türkiye’de İç Göçleri Belirleyen Faktörlerin Modellenmesi,” International Conference on Eurasian Economies, 12-14 October 2011, Beykent University & Kırgızistan-Turkiye Manas University, Bishkek, Kyrgyzstan, In the Proceeding Book, pp. 319-324.

4. Bildirici, M., Ersin, Ö., Onat, I. Ş. (2014), “Baltic Dry Index as a Leading Economic Indicator: A Nonlinear Volatility Analysis,” ICEF- Istanbul Conference on Economics and Finance, 2014, 8-9 Sept. 2014, Yıldız Technical University, İstanbul, Turkey, 1-20.

5. Bildirici, M., Ersin, Ö., Türkmen, C., Yalçınkaya, Y. (2012), “İşsizliğin Kalıcılık Etkisi,” ICEF-Istanbul Conference on Economics and Finance, 15 June, 2012.

6. Bildirici, M., Ersin, Ö., Yalçınkaya, Y., Türkmen, C. (2012), “Türkiye’de İşsizliğin Ekonomik Krizler Sonrasındaki Yapısal Dönüşümü,” ICEF-Istanbul Conference on Economics and Finance, 15 June, 2012.

7.Bildirici, M., Ersin, Ö. (2012), “Support Vector Machine GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets,” 13. Econometrics, Statistics and Operational Research Symposium, Gazi Magosa, North Cyprus, 21 May. 2012.

8. Ustabaş, A., Ersin, Ö. (2016), “The Effects of R&D and High Technology Exports on Economic Growth: A Comparative Cointegration Analysis for Turkey and South Korea,” INTERNATIONAL CONFERENCE ON EURASIAN ECONOMIES 2016, Kaposvár – Hungary 29-31 August 2016, pp. 44-55. 6.3. Published Books and Book Chapters in International (and in National) Books

Books:

1. Bildirici, M., Alp, E., Ersin, Ö. ve Ü. Bozoklu (2010). İktisatta Kullanılan Doğrusal Olmayan Zaman Serisi Yöntemleri. 1. Basım. Türkmen Yayın Evi: İstanbul. ISBN: 9786054259267. (National)

Book Chapters:

1. Ersin, Ö. (2015), “Küresel Krizin Avrupa Birliği Üzerindeki Etkileri: Parasal Birlik Altında İktisat Politikalarına İlişkin Bir Değerlendirme,” Ekonomik Kriz ve Avrupa Birliği, içinde 3. Bölüm, DERİN Yayınları: İstanbul, ss. 57-90. (National).

2. Bildirici,M., Parasız, İ., Aykaç-Alp, E., Ersin, Ö. (2014), “Piyasa Baskınlık Endeksi ve Enflasyon: 2011-2012 Para Politikalarının Analizi,” Prof.Dr. İlker Parasız’a Armağan – Bir Duayen ile İktisat ve Finansı Çok Boyutlu Düşünmek içinde, Bölüm 1, Efil Yayınevi: İstanbul, ss.37-58. (National)

3. Bildirici, M., Ersin, Ö., Kökdener, M. (2010), “The Relationship among Political Instability, Consanguineous Marriage, Genetic Structure and Economic Development: Panel Cointegration Regression, Panel Cointegration MLP Neural Network and Panel SVM Models,” Progress in Economics Research Vol. 21, içinde Chapter 1. pp. 1-61, Ed. Thomas L. Wouters, NOVA Science Publishers: New York, ABD. (International)

6.4. Published Articles on National Refereed Journals

6.5. Articles Presented and Published at National Conferences

1. Ersin, Ö. (2010), “Türkiye’de İç Borçlanmanın Enflasyonist Etkilerinin Test Edilmesi: STAR Yumuşak Geçişli Otoregresif Modeli,” 11. Ekonometri ve İstatistik Sempozyumu, 28-30 Mayıs, 2010, Sakarya, Türkiye, Sempozyum Bildiri Kitabı içinde, ss. 369-394.

6.6 Other Publications Discussion papers and working papers

1. Bildirici, Melike and Ersin, Ozgur Omer, Support Vector Machine GARCH and Neural Network GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets (May 22, 2012). Available at SSRN: https://ssrn.com/abstract=2227747 or http://dx.doi.org/10.2139/ssrn.2227747

2. Bildirici, M., Parasiz, İ., Ersin, O., Aykac Alp, E. (2012), “Psychological Dominance, Market Dominance and Their Impacts on Price Stability in Turkey,”Available at SSRN: http://ssrn.com/abstract=2143580

3. Bildirici, M. and Ersin, O. (2012a), “Markov Switching Artificial Neural Networks and Volatility Modeling with an Application to a Turkish Stock Index”. Available at SSRN: http://ssrn.com/abstract=2118917

4. Bildirici, M., Ersin, O., (2012b), “Nonlinear Volatility Models in Economics: Smooth Transition and Neural Network Augmented GARCH, APGARCH, FI-GARCH and FIAGARCH Models,” Available at SSRN: http://ssrn.com/abstract=2118916

5. Bildirici, M., Ersin, O., (2012c), “Nonlinear Volatility Models in Economics: Smooth Transition and Neural Network Augmented GARCH, APGARCH, FI-GARCH and FIAGARCH Models,” MPRA Working Papers WPRA Paper No. 40330, pp. 1-37. [http://mpra.ub.uni-muenchen.de/40330/] (12.09.2012)

6. Bildirici, M., Ersin, O., (2012c), “Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns,” Available at SSRN: http://ssrn.com/abstract=2125855

7. Ersin, O., (2011), “Fiscal Theory of Price Level and An Analysis of the Recent Testing Methodologies of the Theory (in Turkish)”. Available at SSRN: http://dx.doi.org/10.2139/ssrn.1949448

(Çalışmanın eski versiyonu şu isimde yayımlanmıştır: Ersin, Ö. (2009), “Fiyatlar Genel Düzeyine İlişkin Maliye Teorisi ve Teorinin Test Edilmesine Yönelik Son Gelişmelerin Bir Analizi,” Yildiz Technical University, Department of Economics, Discussion Paper Series, WP no. 0011. IDEAS-REPEC).

8. Bildirici, M., Ersin, O., Koktener, M. (2009a), “The Role of Consanguineous Marriage on the Success of Asia and the Failure of Africa: Panel Neural Network Analysis,” Available at SSRN: http://dx.doi.org/10.2139/ssrn.1490883

9. Bildirici, M., Ersin, O., Koktener, M., (2009b). “Genetic Structure, Consanguineous Marriages and Economic Development.” Available at SSRN: http://dx.doi.org/10.2139/ssrn.1444046

10. Bildirici, M., Ersin, O., Koktener, M., (2009c), “Consanguineous Marriages on Political Instability, Economic Development and Growth: An Empirical Analysis,” Available at SSRN: http://dx.doi.org/10.2139/ssrn.1377822 7.Projects

1. International Conference on Eurasian Economies, 2010. Beykent University, İstanbul, Türkiye, 4-5 Nov. 2010. Görevi: Organizasyon Komitesi Üyesi.

2. International Conference on Eurasian Economies, 2011. Kırgızistan Türkiye-Manas University, Bişkek, Kırgızistan, 12-14 Oct. 2011. Status: Organization Committee Member.

3. International Conference on Eurasian Economies, 2012. Kazakistan Turan Üniversitesi, Almata, 11-13 Oct. 2012. Görevi: Status: Organization Committee Member.

4. International Conference on Eurasian Economies, 2013. Saint Petersburg, 17-18 Sept. 2013. Status: Organization Committee Member.

5. International Conference on Eurasian Economies, 2014. Üsküp, Makedonya, 1-3 July 2014. Status: Organization Committee Member.

6. International Conference on Eurasian Economies, 2015. Kazan, Rusya, 9-11 Sept. 2015. Status: Organization Committee Member.

8.Administrative Duties

Beykent University, The Faculty of Economics and Administrative Sciences, Dept. of Economics (English thought), Head of the Dept. (2015 - continues). 9. Membership of Scientific Foundations

1. Turkish Economic Association (TEA), Türk Ekonomi Kurumu (TEK), Ankara, Turkey.

2. American Economic Association, AEA, Nashville, TN, USA.

3. The Society of Economic Measurement, (Collaboration of Carnegie Mellon Uni., Pittsburg-Center for Financial

Stability, NY; the University of Kansas, KS, USA)

4. Turkish National Committee of Regional Science (Bölge Bilimi Türk Milli Komitesi, BBTMK), Istanbul Tech. Uni., Istanbul, Turkey.

5. Eurasian Economists Association (EEA), Avrasya Ekonomistler Derneği, Istanbul, Turkey.

6. Eurasia Business and Economics Society, EBES, İstanbul, Turkey.

7. Selected referee (as a reviewer) work in international journals: Transactions on Neural Networks Systems (SSCI), Journal of Petroleum Science and Engineering (SCI), Journal of Renewable and Sustainable Energy (SCI-Expanded), Economic Research-Ekonomska Istraživanja (SSCI), Doğuş Üniversitesi Dergisi (ECONLIT), Öneri Dergisi-Marmara Üniversitesi (EBSCO). 10.Rewards 1. The Scientific and Technological Research Council of Turkey (TUBITAK), Research Abroad Scholarship (for 11 of the SCI/SSCI indexed articles between 2009-2015 listed in section 6.1); 2. Beykent University, BEDEK International Research Reward, (for SCI,SSCI indexed articles between 2009- 2016); 3. Eastern Mediterranean University, Education grant given for the GPA record to the high-honor students, 1997-2001. 11. Undergraduate and Master Degree Courses Lectured in the Last Two Years

Academic Year

Term

Course Name Hours in a week Theoretical/Applied

No.of Students

2016-2017

Fall

Development Economics (Dept. of. Econ, in EN.) 3 72

International Economics I (Dept. of. Econ, in EN.) 2 60

Econometrics I (in Eng.to the dept. of Econ. in En.) 3 72

Econometrics I (in TR. to the dept.of Econ,TR) 3 107

Intro. to Economics (Dept. of. Econ, in EN.) 3 50

Intro. to Economics (Dept. of. Econ, in TR.) 3 50

Economics Research Project 0/3 16

Spring

Econometrics II (in Eng. to the dept. of Econ, En) 3 60

Econometrics II (in Tr. to the dept. of Econ,Tr.) 3 75

Applied Econometrics and Package Programs (Dept. of Econ in En.)

1/2 30

Applied Econometrics and Package Programs (Dept. of Econ in En.)

1/2 30

Graduation Project of Economics 0/3 33

2015-2016

Fall

Development Economics (Dept. of. Econ, in EN.) 3 70

International Economics I (Dept. of. Econ, in EN.) 2 60

International Trade Theory (Dept. of Int.’l Trade and Business, in EN.)

3 60

Econometrics I (in Eng.to the dept. of Econ., En.) 3 70

Econometrics I (in TR. to the dept.of Econ,TR) 3 75

Spring

Macroeconomics (to the dept.’s of Econ. and Banking and Fin., in En.)

3 60+60

International Economics II (Dept. of. Econ, in EN.) 3 60

Econometrics II (in Eng.to the dept. of Econ., En.) 3 75

Econometrics II (in TR. to the dept.of Econ,TR) 3 97

Applied Econometrics and Package Programs (Dept. of Econ in En.and in TR.)

1/3 55+55

Notes. The courses given before 2015-2016 are not listed. The courses given in the past include Game Theory (in En. and Tr.), Industrial Economics (En.) Quantitative Methods I and II (En.), Economic Growth (En.), Financial Institutions and Markets (En.), Capital Markets (En.) and Economics I and II courses.

Foreign Languages English: Üniversitelerarası Kurul Yabancı Dil Sınavı, ÜDS, 91.25/100 Eastern Mediterranean University, Dept. of Economics (100% English) German: Cağaloğlu Anatolian Gymnasium, in German. Computer Software and Package Programs

Matlab, Eviews, STATA, Oxmetrics, Winrats, Scilab, R, MS Office, Linux. Contact Information Adres: T.C Beykent Üniversitesi, Şişli-Ayazağa Yerleşkesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü, İSTANBUL. Uni. Offfice: +90 212 867 51 46; E-mail: ozgurersin@beykent.edu.tr

Appendix. Citations* * As of March 2017,the total no. of citations are 220. Statistics are given below.

Table 1. Google Scholar Citation Results Fig. 1. Citations, Jan. 2008-March. 2017

Citation indices All Since 2012

Citations 220 168

h-index 7 6

i10-index 3 3

Source: Google scholar search results, [https://scholar.google.com.tr/citations?hl=en&user=BYiSeRoAAAAJ&view_op=list_works] (Access date: 22.02.2017) *If my own citations that were cited in my own articles and the citations given by my co-authors in their own papers are to be excluded, the ‘net’ no. of citations becomes 149. These articles and the citations are given below. The cited study: Bildirici, M, Ö. Ersin (2009), “Improving Forecasts of GARCH Family Models with the Artificial Neural Networks: An Application to the Daily Returns in Istanbul Stock Exchange,” Expert Systems with Applications, Vol. 36, Issue 4, 7355-7362. (SCI). 1. Guresen, E., Kayakutlu, G., Daim, T.U. (2011), "Using Artificial Neural Network Models in Stock Market Index Prediction," Expert Systems with Applications 38 (8), pp. 10389-10397 (SCI) 2. Ou, P., Wang, H.(2010a), "Predicting Stock Market Volatility by Bayesian Treed Gaussian Processes Based on GARCH Model," ICAMS 2010 - Proceedings of 2010 IEEE International Conference on Advanced Management Science 1, art. no. 5553120, pp. 440-444. 3. Ou, P.H., Wang, H. (2010b), "Predicting GARCH, EGARCH and GJR Based Volatility by the Relevance Vector Machine: Evidence from the Hang Seng Index," International Research Journal of Finance and Economics 39, pp. 46-63. (SCI). 4. Ou, P., Wang, H.(2010c), “Predict GARCH Based Volatility of Shangai Composite Index By Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines,” Journal of Mathematics Research Vol.2 No.2. (EBSCO, PROQUEST) 5. Ou, P., Wang, H. (2010d), “Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets,” International Journal of Economics and Finance 2 (1), ss. 51-64. (EconLit, EBSCO, PROQUEST) 6. Ou, P., Wang, H. (2011a), "Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China," Advanced Materials Research 230-232, pp. 953-957 (ISI Web of Science, SCOPUS) 7. Ou, P., Wang, H.(2011b), "Volatility Prediction by Treed-Gaussian Process with Limiting Linear Model," International Journal of Modeling and Simulation 31 (2), pp. 166-174 . (CSA Cambridge Scientific Abstracts) 8. Ou, P., Wang, H.(2011c), “Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models,” Proceedings of the World Congress on Engineering 2011 Vol I, WCE 2011, July 6 - 8, 2011, London, U.K. 9. Jhao-Da Huang (2009), The Stock Returns Volatility in International Airlines, National Koahsiung First University of Science and Technology, Department of Money and Banking, Master Thesis, (Yüksek Lisans Tezi) http://ethesys.nkfust.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0604109-173709 (16.11.2011). 10. Hossain, A., Nasser, M. (2011) ,"Comparison of the Finite Mixture of ARMA-GARCH, Back Propagation Neural Networks and Support-Vector Machines in Forecasting Financial Returns," Journal of Applied Statistics 38 (3), pp. 533-551. (SCI) 11. Behravesh, M. (2011), "Forecasting Stock Price of Iranian Major Petrochemical Companies," African Journal of Business Management 5 (1), pp. 7-12. (SCI) 12. Das, R.T., Ang, K.K., Quek, C. (2010), "A Synergy of Econometrics and Computational Methods (GARCH-RNFS) for Volatility Forecasting," 2010 IEEE World Congress on Computational Intelligence, WCCI 2010i Barcelona, Spain. JUL 18-23, 2010. 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC). Book Series içinde: IEEE Congress on Evolutionary Computation, Basım Yılı: 2010.

13. Azadeh, A., Saberi, M., Anvari, M.(2010), "An Integrated Artificial Neural Network Algorithm for Performance Assessment and Optimization of Decision Making Units," Expert Systems with Applications 37 (8), pp. 5688-5697. (SSCI) 14. Oliveira, A., Ziegelmann, F. (2010), “Um Estudo Comparativo de Redes Neurais e Modelos GARCH para Previsão da Volatilidade de Séries Temporais Financeiras,” 19º SINAPE - Simpósio Nacional de Probabilidade e Estatística, 26 a 30 de julho de 2010, Hotel Fazenda Fonte Colina Verde, São Pedro-SP. http://www.ime.unicamp.br/sinape/sites/default/files/RedesNeuraisGARCHVolatilidade.pdf 15. Oliveira, A. (2010), “Valor em Risco (VaR) para Modelos de Volatilidade Determinística e Estocástica: Um estudo Comparativo pelo “Backtesting”, 19º SINAPE - Simpósio Nacional de Probabilidade e Estatística, 26 a 30 de julho de 2010, Hotel Fazenda Fonte Colina Verde, São Pedro-SP. http://www.ime.unicamp.br/sinape/sites/default/files/VaRVolatilidadeDeterministica&Estocastica_0.pdf 16. Merlin, P. (2009), Des Techniques Neuronales Dans L’Alternatif, Universite de Paris I Pantheon-Sorbonne, Docteur en Sciences Economiques de l’Universit´e de Paris I, 2009PA10020. (Doktora Tezi). (278 sf.) http://hal.archivesouvertes.fr/docs/00/45/06/ 49/PDF/Merlin_paul_these.pdf (16.11.2011). 17. Maillet, B. and Merlin, P. (2009a), “Outliers Detection, Correction of Financial Time-Series Anomalies and Distributional Timing for Robust Efficient Higher-Order Moment Asset Allocations” (September 16, 2009). SSRN Discussion Paper No. 1413623. http://ssrn.com/abstract=1413623. 18. Anomalies and Distributional Timing for Robust Efficient Higher-order Moment Asset Allocations," JOURNEE D’ECONOMETRIE DEVELOPPEMENTS RECENTS DE L’ECONOMETRIE APPLIQUEE A LA FINANCE Paris Ouest – Nanterre La Défense, le 25 novembre 2009. 19. Bahrammirzaee, A. (2010), "A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems," Neural Computing and Applications 19 (8), pp. 1165-1195. (SCI Expanded) 20. Zhao, Y., Zhang, Y., Qi, C.(2010), "Futures Margin Forecasting and Simulating Based on Multiscale-IGARCH Model," Journal of Computational Information Systems 6 (6), pp. 1843-1853. (EI Compendex) 21. Zhang, W., Li, H.(2009),"Exchange Rates Forecasting and Comparative Analysis Based on Neural Networks," 1st International Conference on Information Science and Engineering, ICISE 2009 , art. no. 5455440, pp. 4011-4014. ISBN: 978-0-7695-3887-7 DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICISE.2009.572 22. Xu, Jingfeng, Liu, Jian (2011), "Forecasting Volatility of Chinese Composite Index Based on Empirical Mode Decomposition and Neural Network," 2011 International Conference on Economics and Finance Research IPEDR vol.4 (2011), IACSIT Press: Singapore, pp. 218-222. http://www.ipedr.com/vol4.htm 23. Hajizadeh,E., Seifi, A.Fazel Zarandi, M.H., Turksen, I.B. (2012), "A Hybrid Modeling Approach for Forecasting the Volatility of S&P 500 Index Return," Expert Systems with Applications 39 (1), January 2012, 431-436. doi:10.1016/j.eswa.2011.07.033 (Doi numarası alma tarihi: 19.07.2011). (SCI) 24. Chai, J., Liu, JNK (2012), “A reliable system platform for group decision support under uncertain environments,” Reliable Knowledge Discovery, 2012, Springer. 25. Maciel, L., (2012), “A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting,” Revista Brasileira de Finanças, 2012 –Ebscohost. 26. B Wang, H Huang, X Wang (2013), “A support vector machine based MSM model for financial short-term volatility forecasting,”- Neural Computing and Applications, 2013 – Springer 27. Dutta, A., G. Bandopadhyay, and S. Sengupta (2012), “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression,” International Journal of Business & Information 7 (1), pp. 105-136. 28. W Xu, T Li, B Jiang, C Cheng (2012), “Web Mining For Financial Market Prediction Based On Online Sentiments,” Pacific Asia Conference on Information Systems, PACIS 2012 Proceedings, pp. 1-14. 29. Yu Zhao, Kun Zhang and Xiaoming Zou (2012), “Price volatility in China’s soybean futures markets based on piecewise seasonal-EGARCH-AR model,” African Journal of Business Management 6 (8), pp. 2860-2870. 30. Kia, A.N., M. Fathian, and M. R. Gholamian (2012), “Using MLP and RBF Neural Networks to Improve the Prediction of Exchange Rate Time Series with ARIMA,” International Journal of Information and Electronics Engineering, Vol. 2, No. 4, pp. 543-546. 31. N Nikolaev, P Tino, E Smirnov (2013), “Time-dependent series variance learning with recurrent mixture density networks,” Neurocomputing 122, pp. 501–512

32. Samimi, A. J., K.D. Darabi (2011), “Forecasting Government Size in Iran Using Artificial Neural Network,” Journal of Economics and Behavioral Studies 3(5), pp. 274-278. 33. A. Hossain, M. Nasser (2011), “Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns,” Journal of Intelligent Learning Systems and Applications 2011 (3), pp. 230-241. 34. Cheng C., W. Xu ; J. Wang (2012), “A Comparison of Ensemble Methods in Financial Market Prediction,” Fifth International Joint Conference on Computational Sciences and Optimization (CSO), 23-26 June 2012, pp. 755 – 759. 35. Hasanabadi, S., H. Mayorga, R. V., & Khan, S. (2012)”, Forecasting Return Volatility of Crude Oil Futures Prices using Artificial Neural Networks,” International Research Journal of Applied Finance, 3 (9). 36. Chen, R. W. Yao (2011), “Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China,” Advanced Materials Research (Volumes 230 - 232), pp. 953-957. 37. Fathian, M., & Kia, A. N. (2012), “Exchange rate prediction with multilayer perceptron neural network using gold price as external factor,” Management Science Letters, 2(2). 38. Nikolaev, N. Y., Boshnakov, G. N., & Zimmer, R. (2013), “Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation,” Expert Systems with Applications, 40(6), 2233-2243. 39. Cao, R., Liang, X., & Ni, Z. (2012), “Stock Price Forecasting with Support Vector Machines Based on Web Financial Information Sentiment Analysis,” Advanced Data Mining and Applications Lecture Notes in Computer Science Volume 7713, pp. 527-538, Springer. 40. Ou, P. Wang, H. (2014), “Volatility modelling and prediction by hybrid support vector regression with chaotic genetic algorithms,” International Arab Journal of Information Technology 11(3), pp. 287-292. 41. Bildirici, M., & Alp, S. (2014), “Measurement of filters’ efficiencies and application of NNSFDI method,” Applied Soft Computing. http://dx.doi.org/10.1016/j.asoc.2014.01.016 42. Maciel, L. S., Gomide, F., & Ballini, R. (2012), “An Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting,” Departamento de Engenharia de Computaçao e Automaçao Industrial, Brazil, Working Paper. [http://www.anpec.org.br/encontro/2012/inscricao/files_I/i7-c8c31e28cec1b3d3c645970d141286f2.pdf] 43. Zhao, Y., Zou, X., & Xu, H. (2013), “Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform,” Research Journal of Applied Sciences, Engineering and Technology 5(2): 649-653. 44. Khan, M. A. I. (2011), “Modelling daily value-at-risk using realized volatility, non-linear support vector machine and ARCH type models,” Journal of Economics and International Finance, 3(5), 305-321. 45. de Oliveira, J. D. C. T., & Maia, S. F. (2013), “Modelagem Da Volatilidade Deterministica No Mercado De Derivativo Do Boi Gordo,” XXXII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO, Bento Gonçalves, RS, Brasil, 15-18, Oct. 2012, ss. 1-12. 46. Lahmiri, S. (2012), “An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume,” Management Science Letters, 2(4). 47. Nemes, M. D., & Butoi, A. (2013), “Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction,” Informatica Economica, 17(3). 48. Zhong-yi, X., Si-ming, L., & Zhang-xi, L. (2012), “A hybrid modeling approach for forecasting the volatility of REITs index in US market,” International Conference on Management Science and Engineering (ICMSE), 2012, pp. 1861-1867. 49. Kristjanpoller, W., Fadic, A., & Minutolo, M. C. (2014), “Volatility forecast using hybrid Neural Network models,” Expert Systems with Applications, 41(5), 2437-2442. 50. Bisoi, R., & Dash, P. K. (2014), “A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter,” Applied Soft Computing, 19, 41-56. 51. Meng, L. (2013), “Forecasting the Exchange Rate Based on BP Neural Network in Combination with Simulated Annealing Algorithm,” Advanced Materials Research, 753, 2930-2934. 52. Xu, J., & Liu, J. (2011), “Forecasting Volatility of Chinese Composite Index Based on Empirical Mode Decomposition and Neural Network,” International Proceedings of Economics Development & Research, 4. 53. Pilbeam, K., Langeland, K.N. (2015), “Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts,” International Economics and Economic Policy 12(1), pp. 127-142. 54. Wei Shen, Xin Wu, Tiyong Zhang (2014), “Shanghai Component Stock Index Forecasting Model Based on Data Mining, Advances in Intelligent Systems and Computing Volume 278, 2014, pp 299-305.

55. Nadarajah, S., E. Afuecheta, S. Chan (2014), “GARCH modeling of five popular commodities,” Empirical Economics, July 2014, pp. 1-22. 56. Li, Y. (2014), “Estimating and Forecasting APARCH-Skew-t Model by Wavelet Support Vector Machines,” Journal of Forecasting, Volume 33, Issue 4, pages 259–269, July 2014. 57. Monfared, S.A., Enke, D. (2014), “Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model,” Procedia Computer Science Volume 36, 2014, Pages 246–253, Complex Adaptive Systems Philadelphia, PA November 3-5, 2014. 58. Donglin Chen, Dissanayaka M. K. N. Seneviratna (2014), “Using Feed Forward BPNN for Forecasting All Share Price Index,” Journal of Data Analysis and Information Processing, Vol.2 No.4, November 2014, pp. 87-94. 59. S. Lahmiri, M. Boukadoum (2015), “An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility,” Fluctuation and Noise Letters, 14(1). 60. Gündüz, H., Z. Çataltepe (2014), “Prediction of Istanbul Stock Exchange (ISE) Direction Based on News Articles,” The Third International Conference on Digital Information Processing and Communications (ICDIPC2013), pp.320-330. ISBN: 978-0-9853483-3-5 ©2013 SDIWC 61. Geng, L.Y., Zhang, Z. F. (2014), Support Vector Machines with Improved PSO Algorithms for Range Volatility Forecasting,” Computer Modelling and New Technologies 18(3), 51-56. 62. Gürsoy, M., Balaban, M.E. (2014), “Hisse Senedi Getirilerindeki Volatilitenin Modellenmesinde Destek Vektör Makinelerine Dayalı GARCH Modellerinin Kullanımı,” Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 5(8): 167-186. 63. Gökgöz, F., Sezgin-Alp, Ö. (2015), “Estimating the Turkish Sectoral Market Returns via Arbitrage Pricing Model under Neural Network Approach,” International Journal of Economics and Finance 7(1): 154-166. 64. Şenyurt, G., Subaşı, A. (2015), “Effects of Technical Market Indicators on Stock Market Index Direction Forecasting,” Modeling of Artificial Intelligence, 6 (2): 137-149. 65. Gündüz, H., Çataltepe, Z. (2015), “Borsa Istanbul (BIST) daily prediction using financial news and balanced feature selection,” Expert Systems with Applications, 31 July 2015, Doi:10.1016/j.eswa.2015.07.058. 66. Kotkatvuori-Örnberg, J. (2016), "Measuring actual daily volatility from high frequency intraday returns of the S&P futures and index observations,"Expert Systems with Applications 43, January 2016, pp. 213–222. 67. Kumar, M., Das, S., Govil, S. (2015), “Analysis of Stock Volatility Clustering Using ANN,” Information Resources Management Journal 28 (2), April 2015, pp. 32-45. 68. Emekter, R., Jirasakuldech, B. (2016), “A Study of Nonlinear Dynamics in Equity Market Index: Evidence from Turkey,” Business and Economics Research Journal 7 (1), pp. 1-19. 69. Das, R.T., Ang, K.K., Quek, C. (2016), “isRSPOP: A novel incremental rough set-based pseudo outer-product with ensemble learning,” Applied Soft Computing, 2016 available online: 3. May 2016. doi:10.1016/j.asoc.2016.04.015 70. Arnerić, J., Poklepović, T. (2016), "Nonlinear Extension of Asymmetric Garch Model within Neural Network Framework," The Sixth International Conference on Computer Science, Engineering and Information Technology, pp 101-111. 71. Geng, L., Yigang, L., Zhang, Z., Shi, X. (2016), "Forecasting Range Volatility using Support Vector Machines with Improved PSO Algorithms," TELKOMNIKA 14 (3A), pp. 208-216. 72. Kristjanpollera, W., Minutolo, M.C. (2016), "Forecasting volatility of oil price using an artificial neural network-GARCH model," Expert Systems with Applications Vol. 65, 15 December 2016, pp.233–241. 73. Lu, X., Que, D., Cao, G. (2016), "Volatility Forecast Based on the Hybrid Artificial Neural Network and GARCH-type Models," Procedia Computer Science 91, pp. 1044-1049. 74. Ozturk, H., Erol, U., Yuksel, A. (2016), “Extreme Value Volatility Estimators and Realized Volatility of Istanbul Stock Exchange: Evidence from Emerging Market,” International Journal of Economics and Finance 8(8), pp.71-83. 75. Xunfa Lu, Danfeng Que, Guangxi Cao (2016), "Volatility Forecast Based on the Hybrid Artificial Neural Network and GARCH-type Models," Procedia Computer Science (ITQM 2016) Vol. 91, 2016, pp. 1044–1049 76. Zhang,H., Sun, RY (2016), “Parameter Analysis of Hybrid Intelligent Model for the Prediction of Rare Earth Stock Futures,” IEEE 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp.835-839. 77. Lahmiri, S. (2017), "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Volume 471, 1 April 2017, Pages 387–395. 78. Inani,S.K., Tripathi, M. Kumar, S. (2016), "Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?" The Journal of Prediction Markets Vol. 10, Issue 2.

79. Beluco, A., Bandeira, D.L., Beluco, A. (2017), “Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model,” Journal of Risk and Financial Management 10 (6), pp. 1-13. Doi:10.3390/jrfm10010006. The cited study: Bildirici, M., Ersin,Ö. (2015), “An Investigation of the Relationship between the Biomass Energy Consumption, Economic Growth and Oil Prices,” Procedia-Social and Behavioral Sciences Vol. 210, pp. 203-212. (SDI) 1. Adewuyia,A.O., Awodumi, O.B.(2017), "Renewable and non-renewable energy-growth-emissions linkages: Review of emerging trends with policy implications," Renewable and Sustainable Energy Reviews, Vol. 69, March 2017,pp. 275–291. The cited study: Bildirici, M., Ersin, Ö. (2013), Forecasting oil prices: smooth transition and neural network augmented GARCH family models, Journal of Petroleum Science and Engineering, 109 (2013), pp. 230–240 1. Zhang, J-L., Zhang, Y-J., Zhang, L. (2015), “A Novel Hybrid Method for Crude Oil Price Forecasting,” Energy Economics doi:10.1016/j.eneco.2015.02.018 10 March 2015. 2. Zou, Y., Yu, L., He, K. (2015), "Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics," Entropy 2015, 17(10), 7167-7184. 3. Elisete Alexandra Coelho Ascenso (2016), "A Assimmetria Na Volatilitade Das Rendibilidades Do Preço Do Crude," Institudo Politechnico De Lisboa, Master Thesis, Lisboa, Portugal, pp.1-101. 4. Kristjanpollera, W., Minutolo, M.C. (2016), "Forecasting volatility of oil price using an artificial neural network-GARCH model," Expert Systems with Applications Vol. 65, 15 December 2016, pp.233–241. The cited study: Bildirici, M., Ersin, Ö. (2014), “Nonlinearity, Volatility and Fractional Integration in Daily Petrol Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models,” Romanian Journal of Economic Forecasting, 2014 Issue 3. (SSCI) 1. Bein, A.M., Aga, M. (2016), “ON THE LINKAGE BETWEEN THE INTERNATIONAL CRUDE OIL PRICE AND STOCK MARKETS: EVIDENCE FROM THE NORDIC AND OTHER EUROPEAN OIL IMPORTING AND OIL EXPORTING COUNTRIES,” Romanian Journal of Economic Forecasting – XIX (4) 2016, pp. 115-134. 2. M. Monge, L.A. Gil-Alana, F. P. de Gracia (2016), "Crude oil price behaviour before and after military conflicts and geopolitical events," EnergyVolume 120, 1 February 2017, Pages 79–91. The cited study: Bildirici, M.; Ersin, O. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns. Sci. World J. 2014, doi:10.1155/2014/497941. 1. M. H. Ahmadi, M-A. Ahmadi, M. Mehrpooya and M.A. Rosen (2015), “Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine,” Sustainability 2015, 7, pp. 2243-2255; doi:10.3390/su7022243 2. Billio, M., Casarin, R., Osuntuyi, A. (2014), “Efficient Gibbs Sampling for Markov Switching GARCH Models, Computational Statistics and Data Analysis (21 April 2014), doi:10.1016/j.csda.2014.04.011 3. Arnerić, J., Poklepović, T. (2016), "Nonlinear Extension of Asymmetric Garch Model within Neural Network Framework," The Sixth International Conference on Computer Science, Engineering and Information Technology, pp 101-111. The cited study: Bildirici, M. and Ersin, O. (2012a), “Markov Switching Artificial Neural Networks and Volatility Modeling with an Application to a Turkish Stock Index”. Available at SSRN: http://ssrn.com/abstract=2118917 1. Pérez, G.A. (2015), “MODELING OF DUAL LISTED CHINESE STOCKS WEAK ARBITRAGE ENVIRONMENT,” (Doktora Tezi) UNIVERSIDAD COMPLUTENSE DE MADRID, FACULTAD DE CIENCIAS ECONÓMICAS Y EMPRESARIALES, pp.1-483. The cited study: Bildirici, M., Ersin, Ö. (2015), "Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach," Petroleum Science, 2015 12 (3), pp. 534-552. 1. He, K., Zha, R., Chen, R., Lai, K.K. (2016), “Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology,” Entropy 18 (170), May-2016, 1-18.

The cited study: BİLDİRİCİ, M. ve Ö.Ö.ERSİN (2012), “Nonlinear Volatility Models in Economics: Smooth Transition and Neural Network Augmented GARCH, APGARCH, FIGARCH and FIAPGARCH Models”, Munich Personal RePEc Archieve, MPRA Paper No: 40330. 1. Gürsoy, M., Balaban, M.E. (2014), “Hisse Senedi Getirilerindeki Volatilitenin Modellenmesinde Destek Vektör Makinelerine Dayalı GARCH Modellerinin Kullanımı,” Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 5(8): 167-186. 2. Arnerić, J., Poklepović, T. (2016), "Nonlinear Extension of Asymmetric Garch Model within Neural Network Framework," The Sixth International Conference on Computer Science, Engineering and Information Technology, pp 101-111. The cited study: Bildirici, M., Alp, E.A., Ersin, O.O. (2010), "TAR-Cointegration Neural Network Model: An Empirical Analysis of Exchange Rates and Stock Returns," Expert Systems with Applications 37 (1), pp. 2-11. 1. Bahrammirzaee, A. (2010),”A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems”, Neural Computing and Applications 19(8), 1165-1195. (SCI EXTENDED) 2. Jagric, V., Kracun, D., Jagric, T. (2011), "Does Non-linearity Matter in Retail Credit Risk Modeling?" Finance a Uver - Czech Journal of Economics and Finance 61 (4), pp. 384-402. (SCI) 3. Antari, J., Chabaa, S., Zeroual, A. (2011), "Modeling Nonlinear Real Processes with ANN Techniques," International Conference on Multimedia Computing and Systems -Proceedings , art. no. 5945665. 4. Pacelli, V., Bevilacqua, V. Azzollini, M. (2011), "An Artificial Neural Network Model to Forecast Exchange Rates," Journal of Intelligent Learning Systems and Applications, Vol.3 No.2, 57-69. (EBSCO, PROQUEST) 5. Pacelli, V. (2012), “Forecasting exchange rates: A comparative analysis,” International Journal of Business and Social Science, 3(10), pp. 145-156. 6. Fathian, M., & Kia, A. N. (2012), “Exchange rate prediction with multilayer perceptron neural network using gold price as external factor,” Management Science Letters, 2(2). 7. Gómez-Ramos, E., & Venegas-Martínez, F. (2013), “A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?” Analítika, Revista de análisis estadístico 3 (2013), pp. 7-15. 8. Özkan, F. (2013), “Comparing the forecasting performance of neural network and purchasing power parity: The case of Turkey,” Economic Modelling, 31, 752-758. 9. Ardiansyah, S., M. Abdul Majid, J.M. Zain (2014), “Foreign exchange forecasting by using artificial neural networks: A survey of literature,” NCON-PGR 2012, UNIVERSITI MALAYSIA PAHANG, KUANTAN 8th – 9th September 2012, pp. 1-5. 10. Anderson Luiz Coan, Marislei Nishijima, Fátima Nunes (2013), “Computational techniques and price tests for determining relevant markets: a systematic review,” Revista da Associação Mineira de Direito e Economia,vol. 10, 2013, pp. 275-300. 11. Pacelli, V. (2014), “Consulenza finanziaria e ottimizzazione di portafoglio, Come gestire la relazione con l’investitore in tempo di crisi,” Banca E Mercati. 124, 2014, pp.1-214. 12. Tkáč, Michal, Verner, Robert (2015), "Artificial neural networks in business: Two decades of research," Applied Soft Computing Vol. 38 Jan. 2016, pp. 788-804. (doi:10.1016/j.asoc.2015.09.040) 13. Wegener, C., von Spreckelsen, C., Basse, T., von Mettenheim, H-J. (2015), “Forecasting Government Bond Yields with Neural Networks Considering Cointegration,” Journal of Forecasting 35(1), pp. 86-92. 14. Hasanabadi, S., H. (2014), “An Application of Artificial Neural Networks in Forecasting Future Oil Price Return Volatilities,” University of Regina Working Paper [http://ourspace.uregina.ca/handle/10294/5736] (Erişim: 24.05.2016). 15. ani,S.K., Tripathi, M. Kumar, S. (2016), "Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?" The Journal of Prediction Markets Vol. 10, Issue 2. The cited study: Bildirici, M., Ö. Ö. Ersin (2005), “Fiscal Theory of Price Level and Economic Crises: The Case of Turkey” Journal of Social and Economic Research JESR, Vol 7(2), pp. 81-114 (ECONLIT). 1. Ayoub, H., Creel, J., & Farvaque, E. (2008a), "Détermination du Niveau des Prix et Finances Publiques : Le Cas du Liban, 1965 – 2005," OFCE Working Paper, 2008 (14). 2. Ayoub, H., Creel, J., & Farvaque, E. (2008b), "Détermination du Niveau des Prix et Finances Publiques : Le cas du Liban, 1965 – 2005," Dulbea University Working Paper No: 08-10.RS. http://dev.ulb.ac.be/dulbea/documents/1242.pdf

3. Ayoub, H., Creel, J., & Farvaque, E. (2008c), "Détermination du Niveau des Prix et Finances Publiques : Le Cas du Liban, 1965 – 2005," Revue D'économie du Développement, Vol. 22 (3), pp. 115-141. doi: 10.3917/edd.223.0115. (ECONLIT). 4. Attiya Y. Javid, Umaima Arif and Abdulsattar (2008), “Testing the Fiscal Theory of Price Level in Case of Pakistan, The Pakistan Development Review 47 : 4 Part II (Winter 2008) pp. 763–778. (ECONLIT). 5. Al Yahyaei, Qais Issa (2011), The Relevancy of the US Dollar Peg to the Economies of the Gulf Cooperation Council Countries (GCC), PhD Thesis, University of Glasgow, Faculty of Law, Business, and Social Sciences, Department of Economics, http://theses.gla.ac.uk/2643/ 6. Özen, E., Şahin, C., & Ünalmış, İ. (2013), “External Financial Stress and External Financing Vulnerability in Turkey: Some Policy Implications for Financial Stability,” Central Bank Review WP No 13/17, ss.1-15. 7. Javid, A. Y., Arif, U., & Sattar, A. (2008), “Testing the Fiscal Theory of Price Level in Case of Pakistan,” The Pakistan Development Review 47 (4), ss. 763-778. The cited study: Bildirici, M., Ö. Ersin (2007), “Domestic Debt, Inflation and Crises: A Panel Cointegration Application to Emerging and Developed Economies,” Applied Econometrics and International Development AEID, 2007-1. (ECONLIT). 1. Cevat Karataş, İdil Uz (2009), “Turkey’s Accession to the European Union and the Macroeconomic Dynamics of the Turkish Economy,” Turkish Studies, Vol 10. Issue: 4 DOI: 10.1080/14683840903384794, pp. 539-557. (SSCI) 2. Munyingi, M. W. (2013), Effect of Domestic Debt on Economic Growth in Kenya (Doctoral dissertation), University of Nairobi, College of Humanities and Social Sciences. [http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/60085] (11.03.2014) 3. Batool, S. A., & Zulfiqar, S. (2013), “Domestic debt: boon or curse? A case of Pakistan,” International Journal of Trade and Global Markets 6 (4), pp. 374-383. 4. Lopes da Veiga, José and Ferreira-Lopes, Alexandra and Sequeira, Tiago (2014), “Public Debt, Economic Growth, and Inflation in African Economies,” MPRA Working Papers no. 57377, pp. 1-30, [http://mpra.ub.uni-muenchen.de/57377/] (25.11.2014) 5. Nastansky, A., Strohe, H. (2015), “Public debt, money and consumer prices: a vector error correction model for Germany,” Ekonometria Econometrics 1(47), pp. 9-31. 6. Van Bon, Nguyen (2015), "THE RELATIONSHIP BETWEEN PUBLIC DEBT AND INFLATION IN DEVELOPING COUNTRIES: EMPIRICAL EVIDENCE BASED ON DIFFERENCE PANEL GMM," Asian Economic and Social Society, Asian Journal of Empirical Research 5(9), 102-116. 7. Mah, G., Mongale, I.P., Mukuddem-Petersen, J. (2016), “Government Debt Reduction in the USA and Greece: A Comparative VECM Analysis,” Eurasian Journal of Business and Economics 2016, 9 (18), 99-112. 8. Sayari, Z., Lajnaf, R. (2017), “Inflation Targeting and Volatility: Panel Evidence,” Theoretical and Applied Economics Vol. 14, Issue 1 (610), pp. 57-68. The cited study: Bildirici, M. Kökdener, M., Ersin, Ö. (2010) “An Empirical Analysis of the Effects of Consanguineous Marriages on Economic Development,” Journal of Family History 35 (4), 368-94. 1. Kwong A, Ng EK, Tang EY, Wong CL, Law FB, Leung CP, Chan A, Cheung MT, To MY, Ma ES, West DW, Ford JM. (2011), A Novel de Novo BRCA1 Mutation in a Chinese Woman with Early Onset Breast Cancer," Familial Cancer Jun, 10 (2), 233-7. (SCI). 2. Kwong, A. Phenotypic and genotypic epidemiological studies of Hong Kong Chinese patients with hereditary breast cancer. (Postgraduate Thesis). The University of Hong Kong (Pokfulam, Hong Kong). [http://hub.hku.hk/handle/10722/188281] (11.03.2014). 3. Bronberg, R., Gili, J., Gimenez, L., Dipierri, J., Camelo, J.L. (2015), “Biosocial correlates and spatial distribution of consanguinity in South America,” American Journal of Human Biology 28(3), pp. 405–411. 4. Dipierri, J.E., Rodríguez-Larralde, A., Barrai, I., Alfaro, E.L. (2016), "CONSANGUINITY BY RANDOM ISONYMY AND SOCIOECONOMIC DEVELOPMENT IN ARGENTINA: A POPULATION STUDY" Journal of Biosocial Science, October 2016, DOI: 10.1017/S0021932016000444 The cited study: Bildirici, M. and Ersin, O. (2012), “Support Vector Machine GARCH and Neural Network GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets (May 22, 2012). Available at SSRN: https://ssrn.com/abstract=2227747 or http://dx.doi.org/10.2139/ssrn.2227747

1. Leung, Jason W. (2016), “Application of Machine Learning: Automated Trading Informed by Event Driven Data,” (Master Thesis), MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), Department of Electrical Engineering and Computer Science, pp. 1-65. The cited study: Ersin, Ö. (2012), “Türkiye’de Reel Döviz Kuru Serisinin Doğrusal Olmayan Ekonometrik Modeller ile İncelenmesi: Band-TAR ve STAR Modelleri,” İktisat, İşletme ve Finans, Cilt 27, Sayı 319 (Ekim), 89-122. 1. Karagöz, T., Saraç, T.B. (2016), “Testing the Validity of PPP Theory for Turkey: Nonlinear Unit Root Testing,” Procedia Economics and Finance 38 ( 2016), 458 – 467. The cited study: Bildirici, M., et Ersin, Ö. Ö. (2012). Support Vector Machine GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets. 13th International Conference on Econometrics, Operations Research and Statistics ICEOS 2012, May 24-26. 1. Charef, F., Ayachi, F. (2016), “A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting,” International Journal of Academic Research in Business and Social Sciences Jan 2016, Vol. 6, No. 1, pp. 244-253. The cited study: Bildirici, M. Kökdener, M., Ersin, Ö. (2009) “An Empirical Analysis of the Effects of Consanguineous Marriages on Economic Development,” SSRN Working Paper versiyonuna yapılan atıflar. (Not. Bu çalışma 2010’dan itibaren SSRN’de yayında değildir) 1. Prades, Jordi Peracaula (2015), NEPOTISME I CORRUPCIÓ Influeix la consanguinitat? - Consanguinity & Corruption, Thesis, Universitat de Girona, İspanya. [https://www.researchgate.net/profile/Jordi_Peracaula_Prades] (22.06.2015) The cited study: M Bildirici, Ö. Ersin, M. Kökdener (2011), "Genetic structure, consanguineous marriages and economic development: Panel cointegration and panel cointegration neural network analyses," Expert Systems with Applications 38 (5), ss. 6153-6163. 1. Su, F., & Shang, H. (2013), “A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction,” Mathematical Problems in Engineering, Vol. 2013, Article ID 875392, pp. 1-6. 2. Navidian, A., Ebrahimitabas, E., Yousefi,N., Arbabisarjou, A. (2015), “A Comparative Study of Intelligence in Children of Consanguineous and Non-consanguineous Marriages and its Relationship with Holland’s Personality Types in High School Students of Tehran,” INTERNATIONAL ARCHIVES OF MEDICINE, Section: Psychiatry and Mental Health, Vol. 8 no: 167,pp.1-8. 3. Shafighi,N., Shaari, A.H., Gharleghi,B., Sarmidi,T., Omar, K. (2016), “Financial integration via panel cointegration approaches in ASEAN+5,” Journal of Economic Studies, 2016 43:1, 2-15. 4. M. Mustafa, R. Zakar, M.Z. Zakar, A. Chaudhry, M. Nasrullah (2017), "Under-Five Child Mortality and Morbidity Associated with Consanguineous Child Marriage in Pakistan: Retrospective Analysis using Pakistan Demographic and Health Surveys, 1990–91, 2006–07, 2012–13,” Matern Child Health J (2017). doi:10.1007/s10995-016-2208-5 The cited study: Bildirici, M., Ersin, Ö., and Kökdener, M. (2009), “Genetic Structure, Consanguineous Marriages and Economic Development,” Social Science Research Network. (SSRN Working Paper versiyonuna yapılan atıflar. [Online] August 4., 2009. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1444046 1. Nielsen, B. M. (2010), “The Social Causes of Malnutrition : A Comparative Study of Two Villages at the Niger River, Yuksek Lisans Tezi, Master en Relations Internationales & Développement, Université de Aalborg au Danemak. http://projekter.aau.dk/projekter/files/37959466/master%20thesis%202010-10-01.pdf 2. Abuqamar, M., Coomans, D., Louckx, F. (2011) “Correlation Between Socioeconomic Differences and Infant Mortality in the Arab World (1990-2009),” International Journal of Sociology and Anthropology Vol. 3(1) pp. 15-21 January 2011. 3. Manuel, T. P. (2012), Effectiveness of Structured Teaching Programme on Knowledge Regarding Ill Effects of Consanguineous Marriage Among Adolescents in Selected Pre University College at Tumkur, Master Thesis, Rajiv Gandhi University of Health Sciences, Bangalore, Karnataka. 4. Prades, Jordi Peracaula (2015), NEPOTISME I CORRUPCIÓ Influeix la consanguinitat? - Consanguinity & Corruption, Thesis, Universitat de Girona, İspanya. [https://www.researchgate.net/profile/Jordi_Peracaula_Prades] (22.06.2015) The cited study: M Bildirici, Ö. Ersin, C. Turkmen, Y. Yalcinkaya (2013), "The Persistence Effect of Unemployment in Turkey: An Analysis of the 1980-2010 Period," Journal of Business, Economics 1 (3).

1. Baştav, L. (2013), “Labor Market Hysteresis and Turkish New Keynesian Phillips Curve (NKPC) (2000-2012),” ECOMOD 2013 - Prague, July 1-3 International Economic Modelling Conference, EEFS 2013 Conference,Berlin, June 16-18. 2. Baştav, L. (2013), “TURKISH INFLATION DYNAMICS: NEW KEYNESIAN PHILLIPS CURVE (2000-2012),” , Bankacılık Düzenleme ve Denetleme Kurumu, pp. 1-26. [http://ecomod.net/system/files/TurkPCmci2.doc] (25.11.2014) 3. Vuslat Us (2014), “Estimating NAIRU for Turkey Using Extended Kalman Filter Approach,” Central Bank Review Vol. 14 (September 2014), pp. 63–94 4. Taşçı, H.M., Özdemir, A.R., Darıcı, B. (2015), “Which Group of Individuals are Subjected to Get Long-Term Unemployment During and After a Recession? Evidence from Turkey,” Sosyoekonomi, Vol. 23, Issue 24, pp.143-162. 5. Kantar, T.M., Aktaş, S.G. (2016), “Spatial Correlation Analysis of Unemployment Rates in Turkey,” Journal of Eastern Europe Research in Business and Economics, Vol. 2016, pp.1-9. 6. Culfaz, E. (2016), “South European Welfare Regime Model and the Turkish Case,” (PhD. Thesis), UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE CIENCIAS ECONÓMICAS Y EMPRESARIALES DEPARTAMENTO DE ECONOMÍA APLICADA I, pp. 1-306. The cited study: M Ercilasun, A. Hiç-Gencer, Ö. Ersin, (2013), “Türkiye’deki İç Göçleri Belirleyen Faktörlerin Modellenmesi, INTERNATIONAL CONFERENCE ON EURASIAN ECONOMIES 2011, SESSION 5B: Büyüme ve Gelişme I, ss. 319-324. 1. Tülümce, S. Y., F. Zeren (2013), “An Analysis of Internal Migrations on the Basis of Provinces in Turkey with the Performance Indicators: A Spatial Probit Model,” Journal of Applied Economic Sciences (JAES) 3 (25), ss. 286-298. 2. Ucan, O., Parlakyıldız, F.M., Öztürk, M.B. (2014), “An Empirical Test of Income Distribution and Migration Relationship: A Case of Turkey,” Asian Economic and Financial Review, 2014, 4(3):355-360. 3. Emirhan, P.N. (2015), “Göreli Yoksulluk ve Bölgeler Arası Göçler: Türkiye Örneği,” Business and Economic Research Journal, 2015 6(2): ss. 79-89. 4. Karpat Çatalbaş, G., Yarar, Ö. (2015), “Türkiye’deki Bölgeler Arası İç Göçü Etkileyen Faktörlerin Panel Veri Analizi ile Belirlenmesi,” Alphanumeric Journal - The Journal of Operations Research, Statistics, Econometrics and Management Information Systems, 3(1): 99-117. 5. Dücan, E. (2016), “Türkiye’de İç Göçün Sosyo-Ekonomik Nedenlerinin Bölgesel Analizi,” Ekonomik ve Sosyal Araştırmalar Dergisi, 2 (2), Yıl 12, 2016. The cited study: Bildirici, M., Ö. Ersin (2010), “The Role of Consanguineous Marriage on the Success of Asia and Failure of Africa: Panel Neural Network Analysis,” Asian-African Journal of Economics and Econometrics, Issue 1-2010, 191-208. (ECONLIT). 1. Ruben Bronberg, Juan Gili, Lucas Gimenez, Jose Dipierri, Jorge Lopez Camelo (2015), "Biosocial correlates and spatial distribution of consanguinity in South America," American Journal of Human Biology, Article first published online: 30 OCT 2015 | DOI: 10.1002/ajhb.22802 (In Press). The cited study: Bildirici, M. Ö. Ersin (2008), “An Empirical Analysis of the Inflationary Effects of Costs of Domestic Debt under Active and Passive Fiscal Policy,” Yapi Kredi Economic Review 19 (1), 3-25. (ECONLIT). 1. Bayrak, M., Kanca, O.C. (2013), “Türkiye’de Kamu Kesimi Açıklarının Nedenleri ve Fiyatlar Genel Düzeyi Üzerindeki Etkileri,” İ.Ü. Siyasal Bilgiler Fakültesi Dergisi No:48 (Mart 2013) ss.91-111.