Özyeğin University, Çekmeköy Campus Nişantepe District, Orman Street, 34794 Çekmeköy - İSTANBUL

Phone : +90 (216) 564 90 00

Fax : +90 (216) 564 99 99

E-mail: info@ozyegin.edu.tr

Levent
Güntay

Assistant Proffessor
International Finance


Doctorate

University of Maryland, Robert H. Smith School of Business Major: Finance, Minor: Economics

Master's

Boğaziçi University, MBA1997

Bachelor's

Boğaziçi University, BS in Electrical and Electronics Engineering



Biography

Levent Güntay is a faculty member in the International Finance Department at Özyeğin University’s School of Business, teaching undergraduate and graduate courses in Finance, Risk Management, and Data Science. He also serves as Director of the Center for Financial Engineering and Academic Director of the Financial Engineering Graduate Program at Özyeğin University. Previously, Dr. Güntay worked as a Senior Financial Economist at the Federal Deposit Insurance Corporation (FDIC), supervising systemically important U.S. banks (2009–2015), and as an Assistant Professor of Finance at Indiana University’s Kelley School of Business (2003–2009). His extensive research on risk management, artificial intelligence in finance, credit scoring, fixed-income modeling, derivatives pricing, and machine learning has been published in journals such as the Journal of Banking and Finance, Journal of Financial Intermediation, and Journal of Financial Services Research, significantly contributing to the Basel III regulatory reforms following the 2008 global financial crisis. Dr. Güntay holds a Ph.D. in Finance from the University of Maryland, College Park, and received his B.S. in Electrical and Electronics Engineering and M.A. in Business Administration from Boğaziçi University.

Research

Refereed Publications

  • Güntay, L., Jacewitz, S., & Pogach, J. (2024). A prudential paradox: The signal in (not) restricting bank dividends. Journal of Money, Credit and Banking, 56(2-3), 537-568. (Best paper award semifinalist, Financial Management Association Meetings, 2015)
  • Kupiec, P., & Güntay, L. (2016). Testing for systemic risk using stock returns. Journal of Financial Services Research, 49, 203-227.
  • Bennett, R. L., Güntay, L., & Unal, H. (2015). Inside debt, bank default risk, and performance during the crisis. Journal of Financial Intermediation, 24(4), 487-513. (Best paper award semifinalist, Financial Management Association Meetings)
  • Güntay, L., & Hackbarth, D. (2010). Corporate bond credit spreads and forecast dispersion. Journal of Banking & Finance, 34(10), 2328-2345. (Best paper award, Swiss Society for Financial Market Research Conference, 2008)
  • Unal, H., Madan, D., & Güntay, L. (2003). Pricing the risk of recovery in default with absolute priority rule violation. Journal of Banking & Finance, 27(6), 1001-1025. (Best paper award, Washington Area Finance Conference, 2001)
  • Güntay, L. & Aktuna, M. (2021). Scenario Based Outlier Detection in Financial Institutions: A Study on the Turkish Factoring Sector. BRSA Journal of Banking and Financial Markets, 15 (1), 83-113.
  • Ahi, E., Güntay, L. (2021). The Effect of Kurtosis and Skewness of Share Returns on Probabilities of Bankruptcy and Default, Journal of Business Research - Turk, 13 (4), 3310-3325.

Non-Refereed Publications and Technical Reports

  • Bank for International Settlements. (2015). Assessing the economic costs and benefits of TLAC implementation. ISBN: 978-92-9197-298-2. https://www.bis.org/publ/othp24.pdf
  • Organisation for Economic Co-operation and Development. (2021). OECD Sovereign Borrowing Outlook 2021. https://www.oecd.org/en/publications/oecd-sovereign-borrowing-outlook-2021_48828791-en.html

Book Chapters

  • Ahi, E. & Guntay, L. (2021). Multi-asset portfolio optimization for the Turkish financial market. Research and Reviews in Social, Human, and Administrative Sciences-II. (pp. 149-162). Ankara, Turkey: Gece Publishing.
  • Guntay, L. (2018). Estimation of Implied Recovery and Loss Given Default Rates. Most Recent Studies in Science and Art Volume 1, Arapgirlioğlu, Atik, Elliott, 2018, Ankara Turkey: Gece Publishing.
  • Toraganli,N. & Guntay, L. (2016). Digital Resources for Economics and Finance Courses, The flipped approach to higher education: Designing universities for today’s knowledge economies and societies. (pp.151-155) Emerald Group Publishing

Conference Proceedings

  • Güntay, L., Bozan, E., Tığrak, Ü., Durdu, T., & Özkahya, G. E. (2022, April). An Explainable Credit Scoring Framework: A Use Case of Addressing Challenges in Applied Machine Learning. In 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE) (pp. 222-227). IEEE.

Working Papers

  • “How to Get a Better Smile? A Comparison of the Implied Volatility Models for the Currency Options Market” with Emrah Ahi, under review at the Financial Analyst Journal
  • “Skipping Across the Bosphorus: Post-Jump Retuırns at Ultra-High Frequency” with Emrah Ahi and Halil Bilgin Payze, under review at the Journal of Financial Markets.
  • “Surrogate Models in Credit Decision Making: Balancing Predictive Power with Explainability” with Tolga Durdu and others, targeted for IEEE Transactions on Engineering Management.
  • “The effect of stocks returns on bankruptcy and default probabilities: A study on Borsa İstanbul companies”

Work in Progress

  • “The Determinants of the Local Currency Credit Spread“ with Emrah Ahi, Yaşar Kemal Peştreli and Mehmet Özsoy, drafted and targeted for Journal of International Money and Finance.
  • “Model-based Trading in Sovereign Bond Markets” with Emrah Ahi targeted for Emerging Markets Review.
  • “Estimating the Empirical Pricing Kernel using Machine Learning” with Emrah Ahi and Han Özsöylev
  • “Using Randomized Controlled Experiments to Reduce Investor Behavioral Biases” with Emrah Ahi, Deniz Anginer and Çelim Yıldızhan
  • “A Novel Approach for Implied Recovery Estimation” with Emrah Ahi
  • “Type of Terrorism and Investor Sentiment” with Barış Çağlar
  • “The Valuation of Trust Preferred Securities”

Teaching

FERM 503: Applied Fİnancial Economics, FERM 508: Machine Learning and Deep Learning