Sanja Dudukovic

Professor, International Management
Department Co-Chair, International Management

Ph.D. University of Belgrade, Yugoslavia
M.S. University of Belgrade, Yugoslavia
B.S. University of Belgrade, Yugoslavia

Office: Lowerre Academic Center, North Campus 4
Phone: +41 91 986 36 34
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Profile:

Since 1991, Dr. Dudukovic has taught a number of business courses, including Management Science, Quantitative Methods and Dynamic Forecasting, and Statistics. Her degrees include a B.S. in Technology, an M.S. in Economics and a Ph.D. in Statistics. Her long-term theoretical research interests include Non Gaussian Time Series Modeling, Entropy Maximization and Information Theory. Since 1997, her empirical research interests cover the fields of Financial Modeling, Credit Spread Modeling, Causality Testing and Stock Market Volatility Forecasting. She has published numerous publications on Non Gaussian Time Series Analysis and Volatility Forecasting, and has considerable private-sector experience in Management Information System Development. She has a particular interest in undergraduate research and is engaged in discovering the methods needed to trigger student research interest by including notions of creativity in philosophy, psychology and logic. She established and directed Franklin's Center for Quantitative Research (CQR) with the aim of achieving results worth presenting at international conferences. She is a member of the Bernoulli Society for Mathematical Statistics, the American Statistical Association, and the IEEC Computer Society.

2018-2019 Courses:

BUS 306	Quantitative Methods and Dynamic Forecasting	FALL 2018
This computer based course covers statistical estimation theory as applied to dynamic and volatile business systems. It addresses sampling distributions, population parameter estimation, hypothesis testing, statistical quality control, statistical model building, multiple regression analysis, Box-Jenkins autoregressive models, seasonal models and dynamic forecasting. Ultimately students are asked to write a research paper: "Dynamic Statistical Modeling in Business and Economics using Web based Resources".
BUS 340	Management Science	FALL 2018
This course is an introduction to quantitative decision making. Problems covered include production scheduling, transportation problems, project management, media selection, optimum decision strategies, portfolio construction and inventory management. Theoretical methods include linear programming, integer linear programming, sensitivity analysis and complex decision trees. Case problems based on the software "Management Scientist" stress real world applications. Students are asked to write a research paper based on the real data analysis.
MAT 201	Introduction to Statistics	FALL 2018
Basic concepts of descriptive statistics, such as random variables, random sampling, histograms, central tendency measures, variance and standard deviation, probability rules, and correlation coefficients, are presented in this course. The most important probability distributions, binomial and normal, are introduced. Inferential statistics and sampling distributions are briefly covered in order to introduce statistical model building and linear regression analysis.
MGT 544	Management Science	FALL 2018
In the first part of this computer-based course, students learn linear programming algorithms and how to apply them for resource allocation in production, investment selection, media selection, transportation planning, job assignments, financial planning, make or buy decision making and overtime planning contexts. In the second part of the course, students learn how to choose the best decision using expected monetary value (EMV), how to make optimum decision strategies under uncertainty by making decision trees, how to evaluate marketing research information, and how to apply project management (PERT) basic steps. Ultimately students are asked to conduct a month-long research and development project to define a real organizational decision strategy.
BUS 306	Quantitative Methods and Dynamic Forecasting	SPRING 2019
In the first part of this course students learn concepts in inferential statistics, its main principles and algorithms. They learn how to apply sampling distributions in the case of business random variables, how to state and test business hypotheses about population mean or proportion differences, how to calculate ANOVA table components, and how to deploy estimation methods to provide information needed to solve real business problems. In the second part of the course, students learn advanced model building methods, algorithms needed to make and test dynamic multiple regression models and time series (ARMA) models. In addition to teaching and learning methods based on the textbook, problem-based learning (PBL) and interactive engagement (IE) are used. Many internet data bases, EXCEL add-ins and EViews are used to enhance IE based learning. Selected SPSS or STATA examples are also provided.
BUS 340	Management Science	SPRING 2019
In the first part of this computer-based course, students learn linear programming algorithms and how to apply them for resource allocation in production, investment selection, media selection, transportation planning, job assignments, financial planning, make or buy decision making and overtime planning contexts. In the second part of the course, students learn how to choose the best decision using expected monetary value (EMV), how to make optimum decision strategies under uncertainty by making decision trees, how to evaluate marketing research information, and how to apply project management (PERT) basic steps. Ultimately students are asked to conduct a month-long research and development project to define a real organizational decision strategy.
MAT 201	Introduction to Statistics	SPRING 2019
This computer-based course presents the basic concepts in statistics: random variables, random sampling, frequency distributions, central tendency measures, variance and standard deviation, kurtosis and skewness, probability rules, Bayes theorem and posterior probability. Important statistical methods like contingency analysis, ANOVA, and correlation analysis are introduced and their algorithms are explained. The most important probability distributions are introduced: Binomial, Poisson, and Normal distribution, as well as the Chebyshev theorem for non-symmetrical distributions. Inferential statistics, sampling distributions and confidence intervals are briefly covered in order to introduce statistical model building and single linear regression and trend analysis. Students learn how to promote the scientific method, how to identify questions, collect evidence, discover and apply tools to interpret the data, and communicate results. EXCEL is used to enhance algorithmic learning. Selected SPSS or STATA examples are also provided.
BUS 340	Management Science	SUMMER 2019
In the first part of this computer-based course, students learn linear programming algorithms and how to apply them for resource allocation in production, investment selection, media selection, transportation planning, job assignments, financial planning, make or buy decision making and overtime planning contexts. In the second part of the course, students learn how to choose the best decision using expected monetary value (EMV), how to make optimum decision strategies under uncertainty by making decision trees, how to evaluate marketing research information, and how to apply project management (PERT) basic steps. Ultimately students are asked to conduct a month-long research and development project to define a real organizational decision strategy.
MAT 201	Introduction to Statistics	SUMMER 2019
This computer-based course presents the basic concepts in statistics: random variables, random sampling, frequency distributions, central tendency measures, variance and standard deviation, kurtosis and skewness, probability rules, Bayes theorem and posterior probability. Important statistical methods like contingency analysis, ANOVA, and correlation analysis are introduced and their algorithms are explained. The most important probability distributions are introduced: Binomial, Poisson, and Normal distribution, as well as the Chebyshev theorem for non-symmetrical distributions. Inferential statistics, sampling distributions and confidence intervals are briefly covered in order to introduce statistical model building and single linear regression and trend analysis. Students learn how to promote the scientific method, how to identify questions, collect evidence, discover and apply tools to interpret the data, and communicate results. EXCEL is used to enhance algorithmic learning. Selected SPSS or STATA examples are also provided.

Journal Publications:

"Testing GARCH and RV Exchange Rate Volatility Models using Hinich Tricorrelations," Oxford Journal on Business and Economics , Vol. 9, No. 2 (2014) ,136-151.

"A Cumulant-based stock market volatility modeling – Evidence from the international stock markets," Journal of Finance and Accountancy , Vol. 17, October 2014, 80-97 .
http://www.aabri.com/manuscripts/141970.pdf

"Credit Spread Modeling: Macro-financial versus HOC approach," Economic Analysis ,2014, Vol. 47, No. 3-4, 53-68
https://ideas.repec.org/a/ibg/eajour/v47y2014i3-4p53-68.html

"Exchange Rate Volatility Forecasting Using Higher Order Cumulant Function." 2013. China-USA Business Review, ISSN 1537-1514, Vol. 12, No. 6, 533-542, ISSN 1573-1514

“A new Approach to Causality Testing”, Journal of Economic Analysis Vol. 44, no. 2011/1-2. EBSCO journal.
http://www.ien.bg.ac.rs/index.php/en/journal-economic-analysis

"Stock Market Volatility Forecasting Using Higher Order Statistics - Evidence from the Belgrade Stock Exchange." Statistical Review, Vol. LVIII, No 3-4, pp.19-30, 2009, ISSN 0039-0534.
http://scindeks.nb.rs/issue.aspx?issue=7757&lang=en

“Suppression of the regular Interference in the presence of Band Limited White Noise, in: Torres: Signal Processing, Theories and Application.” IEEE Press, Elsevier Pub. Comp, Vol. 1, 593-597.

“Hierarchical approach to process industries management", Scientific Review, Vol. XXVII,No 8, 19 -25.

“Theoretical Foundations and Possible Usage of Time Series Analysis", Scientific Review, Vol XXVI , No 2, 37-50.

“Statistical planning of experiments”, Scientific Review, Vol. XXVI, No 10, 167-176.

“Stochastic Model Building Identification”, Scientific Review, Vol. XXVI, No 5, 21-34.

“Reliability Forecasting Using Time Series”, Scientific Review, Vol. XXVIII, No 6, 25-35.

“Stochastic models testing", Scientific Review, Vol. XXVIII, No 8, 17-26.

“Consistent MA Parameter Estimation", Scientific Review, Vol. XXXVIII, No 8.

“Four methods for ARMA Parameter Estimation", Scientific Review, Vol. XXXVIII, No 10.

“AR model Identification Criterion", Scientific Review, Vol XXX, No. 4, 36-45.

Working Papers:

"Testing GARCH and RV Exchange Rate Volatility Models using Hinich Tricorrelations-The case of Exchange Rate Volatility Forecasting," proceedings of the CBEC conference, Cambridge 204, July 1-3, 2014.

"Entropy Loss in Non Gaussian High Frequency Measurement Systems," the 2014 International Conference on Systems and Informatics (ICSAI 2014) ,15-17 November 2014, Shanghai, China .

"Capturing stylized facts of Exchange Rate Volatility Using Higher Order Cumulant Function", Proceedings of the Cambridge Business & Economics Conference (CBEC), July 1-3. Cambridge, UK 2013.
http://www.gcbe.us/2013_CBEC/data/Sanja%20Dudukovic.pdf

“Exchange Rate Volatility Forecasting using Higher Order Cumulants and HF”, Proceedings of the Cambridge Business & Economics Conference (CBEC), June 27-29. Cambridge University, Cambridge, UK, 2012.
http://gcbe.us/2012_CBEC/data/Sanja%20Dudukovic.pdf

"Stock Market Volatility Forecasting - Evidence from the International Stock Markets.", Proceedings of the Cambridge Business & Economics Conference, Cambridge , UK, June 27-30, 2011.
http://www.gcbe.us/2011_CBEC/data/Sanja%20Dudukovic.pdf

”Answering the Skeptics: No, GARCH Exchange Rate Volatility Models Do Not Provide Accurate Forecasts”, Proceedings of the fourth EBES 2011 Conference (Eurasia Business and Economics Society) - Istanbul, June 1-3, 2011, Turkey.

"Spectral Analysis of GDP Shocks in US and BRIC Countries." Proceedings of the 4th Oxford Business and Economics Conference, Oxford, UK, June 28-30, 2010.
http://www.gcbe.us/2010_OBEC/data/Sanja%20Dudukovic.pdf

"Causality Testing Using Higher Order Cumulants." Proceedings of the 3rd Oxford Business and Economics Conference, Oxford, UK, June 23-26, 2009.
http://www.gcbe.us/2009_OBEC/data/Sanya%20Dudukovic.pdf

"Credit Spread Modeling Using Higher Order Cumulants." Proceedings of the Oxford Business and Economics Conference. ISBN: 978-0-9742114-7-3.
http://www.gcbe.us/2008_OBEC/data/Sanya%20Dudukovic%20.pdf

"Harmonic Analysis of the Real Business Cycles." Proceedings of the 2nd Oxford Conference on Business and Economics, Oxford, UK, June 24-27, 2007.
http://www.gcbe.us/2007_OBEC/data/Sanya%20Dudukovic.doc

"Causality between Interest Rate, Budget Deficit and Debt." Presented at the 6th International Conference on Business and Economics, Harvard University, USA, October 15-17, 2006.

"Forecasting Credit Spread Using Macro-Financial Variables." Proceedings of the 5th Global Conference on Business and Economics, Cambridge, UK, July 6-8, 2006. ISBN: 0-9742114-3-7.

“Micro-financial Determinants of Corporate Credit Spread," accepted for presentation at 5th Global Conference on Business & Economics Cambridge, UK, July 6-8, 2006.

“Dynamic Time Series Approach to Credit Spread Modeling," Proceedings of the Fourth Global Conference on Business & Economics, Jun, 26-28, 2005,Oxford, ISBN: 0-9742114-3-5.

“Feedback Between US Composite Index and Credit Spread,” Proceedings of 3rd International Finance Conference, IFC, 3-5 March 2005, Hammamet, Tunisia.

“Feedback between US Composite Index and Credit Spread.” Full paper accepted for the presentation on 3rd International Finance, Tunis, March 2005.

“Dynamic Economic Forecasting by Higher Order Statistics," Global Conference on Business and Economics, June 2005, Oxford University, approved, to be published in the proceedings of the conference. (Research Partnership with Olsen & Associates, Zurich, Switzerland)

“Interest Rate Spread and GDP Growth –GARCH based Causality Test," Preoceedings of Global Conference on Business and Economics, July 7-9, 2004, ISBN 0-9742114-9.

"Extraction of the Band-Limited White Noise in the Presence of Colored Non Gaussian Noise," 3rd WSEAS International Conference on System Theory And Scientific Computation, poster session, Rhodos, Greece, July 28-30, 2003.

“Assessment Data Processing Methods for Recourse Allocation“, presented in the poster session of the Seventh Annual CSU Fullerton Assessment Conference, California State University, poster session, March 12-13, 2003, Fullerton.

“Does GARCH Outperform ARIMA? " Statistical review,Vol. XXXIX, No 3, 23-28,2001, ISBN 0039-0534.

“Causality Between SP500 And Real US GDP," Proceedings of 3rd International Finance Conference in Hammamet, Tunisia, March 2003.

"Stock Market Returns And Real GDP in UK," Proceedings of the Global Conference on Business and Economics , Imperial London, July 2003, ISBN: 0-9742114-1-9

"Extraction of the Narrow Band Volatility from GARCH Noised Stock Market Volatility", International Conference on Physics in Economics, Dublin 1999.

Awards and Honors:

2006: Franklin Faculty Excellence Award: Teaching
2011: Franklin Faculty Excellence Award: Teaching
2012: Franklin Faculty Excellence Award: Teaching

Sanja Dudukovic

Sanja Dudukovic

Professor, International Management Department Co-Chair, International Management

Profile:

2018-2019 Courses:

Journal Publications:

Working Papers:

Awards and Honors:

Professor, International Management
Department Co-Chair, International Management