Dr. Karagozoglu received a B.S. in industrial engineering from Bogazici University, Istanbul, Turkey. He received an M.B.A. from the University of Wisconsin, Oshkosh. He earned an M.Phil. and a Ph.D. in finance at Baruch College of the City University of New York, where he received the Oscar Lasdon Best Dissertation Award for the best dissertation.
Dr. Karagozoglu joined the Zarb School’s Finance Department at Hofstra in 1999. He is the founding Academic Director of the Martin B. Greenberg Trading Room since 2005. He is also the Director of the GARP-Global Association of Risk Professionals’ Hofstra Chapter and serves as the faculty advisor of student organizations HQT-Hofstra Quants & Traders and ALPFA.
Dr. Karagozoglu received the Dean’s Research Award both in 2000 and in 2012, the Dean’s Service Award in 2007 and the Distinguished Teacher of the Year Award in 2009.
Dr. Karagozoglu has been honored as the keynote speaker at the 11th Shanghai Derivatives Market Forum in 2014 and at the 2nd Turkish Derivatives Conference in 2010. He received the inaugural research grant of the Washington, DC. based Institute for Financial Markets in 2010.
Dr. Karagozoglu has taught summer courses at Erasmus University of Rotterdam in the Netherlands and at Korea University. Prior to joining Hofstra, Dr. Karagozoglu taught full time at Baruch College in both graduate and undergraduate finance programs.
Dr. Karagozoglu is a member of the Global Association of Risk Professionals, the International Association for Quantitative Finance, the American Finance Association, the Financial Management Association and has been inducted to the Beta Gamma Sigma Honor Society.
Industry Expertise (3)
Areas of Expertise (7)
Advanced Derivatives Modeling
Time Series Analysis of Financial Data
Advanced Statistical Modeling in Finance
Futures and Options
Dean’s Research Award (professional)
Distinguished Teacher of the Year Award (professional)
Dean’s Service Award (professional)
Dean’s Research Award (professional)
Oscar Lasdon Best Dissertation Award (professional)
Awarded by the Baruch College of the City University of New York
CUNY Baruch College of the City University of New York: PhD 1999
University of Wisconsin Oshkosh: MBA 1994
Bogazici University: BS 1992
- Member of the Global Association of Risk Professionals
- Member of the International Association for Quantitative Finance
- Member of the American Finance Association
- Member of the Financial Management Association
- Beta Gamma Sigma Honor Society - Inductee
Media Appearances (5)
New Distinguished Professor in Finance & Investment Banking: Dr. Ahmet K. Karagozoglu
News @ Hofstra online
'Longtime Zarb faculty member Dr. Ahmet K. Karagozoglu, PhD was recently installed as the C.V. Starr Distinguished Professor in Finance and Investment Banking in recognition of his extensive scholarship achievements and contributions to Hofstra University.'
Henry Schein Top L.I. Company by Sales
Hofstra University finance professor Ahmet K. Karagozoglu said that large companies can have a significant “multiplier effect” on the Long Island economy depending on the number of jobs based locally, the salary levels paid, and the goods and services the corporations purchase from local suppliers.
Senate hearing on high-frequency trading to look at market fairness
Wall Street Journal - MarketWatch online
The Senate turns its focus this week to conflicts of interest and loss of investor confidence in the U.S. markets. The hearing will focus on two specific conflicts of interest:
First, payments by wholesale broker-dealers to retail brokers for their customer orders, known as “payment for order flow;” and “maker-taker” rebates or fees that, depending on the circumstances, exchanges either pay or charge to brokers for executing trades on their platforms.
The second focus is rebates paid to brokers by stock exchanges and other trading venues. There are currently eleven public stock exchanges plus over 200 alternative trading systems, including a large number of “dark pools.” These trading venues offer various rebate structures in order to attract brokers, and orders, to their platforms.
“One of the difficulties these days is that financial markets have changed so much because of the technology,” said Ahmet K. Karagozoglu, finance professor at Hofstra University. “Financial regulators have to catch-up with the pace of technology.”
There are a lot of financial institutions on one side of the argument that are not so fast, and there is a smaller group on the other side of the debate that uses the high-speed systems, notes Karagozoglu.
“The market is divided into fast or not so fast.”
Surge in Cotton Prices Could Hit Your Wallet Hard
Dr. Ahmet Karagozoglu, professor of Business and Finance at Hofstra University said global inventories have been drained.
“Demand for cotton is outpacing the supply. As a result, December cotton futures trading in New York went above $1, which has never happened since 1995,” Karagozoglu said.
News @ Hofstra online
'Dr. Ahmet Karagozoglu, a professor of finance and the academic director of the trading room, arranges the visit, which includes a special ceremony during which Bloomberg executives congratulate the students for their achievement.'
Following the recent global financial crisis, regulators have recognized the importance of stress testing, in part due to the impact of model risk, and have implemented supervisory requirements in both the revised Basel framework and the Comprehensive Capital Analysis and Review (CCAR) program. We contribute to the literature by developing a Bayesian-based credit risk stress-testing methodology, which can be implemented by small-to-medium-sized banks, as well as presenting empirical results using data from the recent CCAR implementations. Through the application of a Bayesian model, we can formally incorporate exogenous scenarios and also quantify the uncertainty in model output that results from stochastic model inputs. We contribute to the model validation literature by comparing the proportional model risk buffer measure of the severely adverse cumulative nine-quarter loss estimate – a common way to estimate, being a measure of statistical uncertainty generated by a model – obtained from our empirical implementation of the Bayesian to the frequentist model. We find it to be 40% higher in the former than in the latter. As for the model validation exercise, the Bayesian model outperforms the frequentist model statistically significantly, according to the cumulative percentage error metric, by 2% (1.5%) over the entire sample (downturn period).
In this article, Jacobs,Karagozoglu, and Naples Layish focus on determining which types of firms are able to successfully remain independent entities through the resolution of their financial distress. The authors empirically investigate the determinants of the process utilized to resolve financial distress (private work-out versus public bankruptcy filing) and also the outcome (liquidation versus reorganization).After developing various qualitative-dependent variable models, they estimate and compare several accounting and economic variables measured at the time of default that they expect can influence the resolution process and outcome.Results reveal the ordered logistic regression specification achieves the best balance between in-sample fit, consistency with financial theory, and out-of-sample classification accuracy, as compared to more elaborate techniques, such as local regression or neural networks.The authors find the public resolution process to be associated with larger firms that have less tangibility, a greater proportion of secured debt in their capital structures, or higher risk measures. The private resolution process is likelier for firms that have more total leverage, greater measures of liquidity, a higher proportion of subordinated debt in their capital structures, or reside in a debtorfriendly bankruptcy court district.Regarding the resolution outcome, the authors find that firms that are more likely to be liquidated than reorganized have greater liquidity, more secured debt, lower cumulative abnormal returns on equity, higher loss given default, a less favorable auditor's opinion, or they will default in a better part of the credit cycle. Finally, firms more likely to be reorganized have greater leverage,more intangible assets, or a prepackaged bankruptcy. They conclude that their model is useful for risk managers and investors who are in the market for distressed or defaulted debt.
Loss given default (LGD) is a critical parameter in various facets of credit risk modeling. This study empirically investigates the determinants of LGD and builds alternative predictive econometric models for LGD on bonds and loans using an extensive sample of most major US defaults in the 1985–2008 period. The authors build simultaneous equation models in the beta-link generalized linear model (BLGLM) class, identifying several that perform well in terms of the quality of estimated parameters as well as overall model performance metrics.
Acknowledgements: Author acknowledges the Institute for Financial Markets grant and summer research support of the Zarb School of Business at Hofstra University. Special thanks are due to John Labuszewski at the CME Group for providing the algorithmic trading and microstructure data as well as for his comments. Research assistance by Vanessa Singh and Xia Zhang is greatly appreciated.
This paper provides evidence on the relation between private-information-based trading and foreign trading activity on the Istanbul Stock Exchange (ISE). We use a recently developed model that utilizes information in volume–return dynamics of individual stocks and show that variables such as size and Tobin's Q explain the extent of speculative activity across firms traded on the ISE. We present evidence supporting the notion that foreign trading activity is associated with informed trading on the ISE. Implications of our findings for emerging...