Program Structure

In the full-time programs lectures can be scheduled in the time zones between 09:00-18:00.

The total number of credits in the program is 90. It includes six (6) compulsory courses of which four (4) in the first semester with seven and a half (7.5) credits each and two (2) in the second semester with six (6) credits each, three ( 3) elective courses in the second semester with six (6) credits each and the writing of a diploma thesis in the third semester with thirty (30) credits.

Before starting the program: two (2) non-credit preparatory courses are offered. The role of the preparatory courses is to prepare the students to follow the Program without possible deficiencies in its knowledge subjects from their undergraduate level, which are prerequisites of the program, to give them the opportunity to better understand their knowledge needs and to deal effectively with any gaps they may have in the required program material.

2. The Full Study Program of taught and examined courses is defined in detail as follows:

1st Semester

Introduction to Statistic

The course introduces and presents basic descriptive statistics measures and charts useful for data exploration, the theory of basic continuous and discrete distributions, and develops the techniques - methodologies for finding point estimators such as maximum likelihood and least squares. Properties of estimators and sampling distributions useful in statistical inference are presented. The construction of confidence intervals and the conduct of hypothesis testing are introduced and demonstrated. Statistical techniques and methods are applied using the R package.

The aim of the course is to present, develop and apply basic concepts of statistics, and to learn students to use appropriate statistical methods, models and techniques needed to analyze data in empirical problems. Upon successful completion of the course, students will be able to: Apply statistical techniques and methods using the R package. Calculate useful descriptive measures and construct appropriate charts. Understand basic distributions and their practical utility. Calculate probabilities using basic distributions. Apply parameter estimation methods such as the maximum likelihood method. Understand the sampling distribution and its utility. Construct confidence intervals and conduct hypothesis tests.

Introduction to Microeconomic Theory

The course examines the basic economic principles of finance. In more detail, it examines the way in which consumers decide how to allocate their income. It also analyzes the way in which companies decide what and in what quantities to produce. The properties of the various market forms are examined and compared according to their characteristics. In many markets firms interact and their analysis is done through game theory. An introduction to game theory and the concept of Nash equilibrium is given. Also, the equilibrium price and quantity are described in markets in which firms compete either by setting quantities or by setting prices.


Industrial Organization and Strategy

The course presents basic principles of Business Economics and focuses on issues of Industrial Organization and Policy. Specifically, it examines the nature and characteristics of the static and dynamic equilibrium of imperfectly competitive markets, i.e. markets in which firms have some degree of monopoly power, compared to the socially optimal and economic policy measures aimed at improving their smooth functioning and efficiency of markets. Finally, business strategies are examined depending on the structure of the markets, the international environment and the macroeconomic conditions in the economy. Upon completion of the course, students are expected to know the appropriate financial tools for understanding and analyzing different markets for products or services. The purpose of the course is to expose students to economic policy problems both from the side of Competition Policy and from the side of Regulatory Policy.

7.5 ECTS

Market Analysis and Portfolio Management

The purpose of the course is to introduce students to the modern tools and techniques of investment analysis and evaluation, which include decision making under certainty and uncertainty, risk pricing, optimal stock portfolio management, and pricing of stocks or other assets. Also, the course includes bond pricing, explains the yield curve, and covers bond portfolio management. The course presents applications of the above methods using software for a better understanding of them in practice. At the end of the course, students will have understood the above tools in investment analysis and will have become familiar with their application in practice. In summary, the course teaches students methods of making investment decisions under certainty and uncertainty, optimal mean-variance portfolios, equity risk pricing based on the CAPM, multifactor equity risk pricing models, based on the APT, bond markets, explanation of the interest rate curve, bond portfolio management techniques, International capital markets and portfolio management.

7.5 ECTS
Analytical and Computational Data for Economists

The course deals with applied econometrics and computational methods using the statistical programming language R (The R Project for Statistical Computing) for the efficient analysis and management of economic data. Topics covered include: applications of descriptive statistics and key plot methods, applications of computational econometric methods to linear and non-linear models, categorical data, linear regression, logistic regression, decision trees, neural networks, cluster analysis and forecasting. Also, the course presents indicative optimization and resampling methods.

7.5 ECTS
Quantitive Methods

The course introduces and presents the fundamental theory of statistical and econometric models, methods and techniques, which are essential in the research and analysis of economic and financial data. First, the theory of regression models, simple and multiple linear regression, is presented. Topics such as variable/model selection, use of dummy variables, and multicollinearity are examined. Emphasis is placed on the application of theory, examining residual hypotheses using diagnostic tests, and the interpretation of results is presented. The theory and practical application of time series analysis models are introduced and presented in detail. A detailed description and presentation of stochastic time series models (ARMA models) is made, and the Box-Jenkins methodology is developed. The course introduces the generalized linear models (logit/probit, log-linear models) used for the analysis of binomial data and frequency data (Poisson data). The break-point models and the basic checks for the existence of structural changes in financial data are presented and developed. Finally, panel data models are presented, as well as the techniques for estimating the parameters of these models. An analytical application of the theory, models and methods to empirical economic and financial problems is made using the R statistical package.

The aim of the course is to learn students how to use appropriate statistical and econometric methods, models and techniques required to analyze economic and financial data. Upon successful completion of the course, students will be able to:

  • Know and apply a wide class of econometric models to useful empirical problems. 
  • Learn the principles of statistical and econometric inference, so that they are able to understand the analysis necessary for a particular data set, and how it can be properly applied.
  • Estimate the parameters of statistical and econometric models.
  • Conduct hypothesis tests and construct confidence intervals for population parameters.
  • Estimate regression models and time series models, construct forecasts and appropriately interpret the results of their analysis.
  • Estimate structural change models and panel models and apply them to empirical economic problems.  
  • Apply, using the R package, econometric models to empirical economic problems and applications.
7.5 ECTS

2nd Semester

Course ECTS

Econometric Applications in Economic and Finance

The course focuses on complete time series analysis: description, modeling, estimation and forecasting, as well as simulation. In detail, topics include: Stationary Time Series Models, Parameter Estimation, Diagnostics and Forecasting, Analysis of Non-Stationary Time Series (unit root problem, concept of cointegration, error correction models, with applications to financial and macroeconomic series). Variable Bound Variance (properties of financial series, ARCH, GARCH, EGARCH models, properties of models, applications to financial series).

Applications of Analytical Methods of Business Finance and Strategy

The course deals with applied topics using analytical methods in microeconomics and macroeconomics-finance, and is organized in 2 parts:

Part 1 - Macroeconomics/Investments: Factor Analysis and forecasts of macroeconomic quantities, direct forecasts (nowcasting), macroeconomic forecasts with big data bases and machine learning methods, evaluation of alternative methods of macroeconomic forecasts and investments, simulation of macroeconomic models and investment strategies, presents the Z- score evaluations and credit risk assessment methods, as well as stress tests.

Part 2 - Microeconomic topics: Experimental design of alternative products/services, empirical decision-making models, revealed preference discrete choice data analysis, stated preference data analysis, scenario-based choice/demand/market share simulation, segmentation, designing preference data collection tools.


Indicative list of elective courses

The elective courses offered each year are decided by the General Assembly of the Department and are mentioned in the study guide of each academic year. *You must attend 3 (three) elective courses
Behavioral Economics

The first part of the course focuses on behavioural theory, which includes decision theory, behavioural game theory with empirical applications in economics and business. There will be an overview/revision of the fundamentals of behavioural microeconomics. In the course of this, students will be participating in actual experiments through surveys and online games. Applications will include, among others, the behaviour of drivers under deferent reputation mechanisms (based on a field study with Beat and Uber in Athens), the effectiveness of Covid-19 lockdown measures based on the behaviour of individuals using data from google mobility data and experimental evidence, and applications in auctions and company takeovers. The second part of the course focuses on quantitative behavioral macroeconomics with applications in economics, business and finance. This part of the course aims to enhance students background with the deep cultural routes of contemporary human behavior. This course combines knowledge from interdisciplinary quantitative research studies such as business, engineering, psychology and anthropology. Among others, applications will include scrapping people preferences data from google, twitter and facebook using R, explaining the rise of the experiences economy (important category in AirBnb), and the importance of culture on the consumption of luxury goods, on savings rate, CEOs firm decisions and investors decisions on stock market participation.


Game Theory and Strategic Decisions with applications in Economics

This course is designed for people in business, for managers. It is as theoretical as necessary for providing an introduction to the science of game theory; and practical in that it offers many applications and case studies to make it attractive to managers in both the commercial and non-profit sectors, as well as to students in business. The chief purpose of this course is to enable the student to set up, study and solve games, especially games that arise in business and economics. To acquire a taste of the type of situations we would be interested in as well as the type of questions we would be asking, think of the following “real-life” situation.

Banking Administration and Risk Management (offered by another MSc)

The financial crisis that broke out in 2007 demonstrated the importance of recognizing and managing the multiple risks faced by financial institutions (FI). The course provides a comprehensive approach to managing the risks faced by FI: identifying, measuring and mitigating them. Emphasis is placed on the role of derivative products in reducing risk. Both internal systems and external prudential rules are covered, seeking solutions to the deficiencies that have led to failures in both self-regulation and supervision of FI.

Corporate Finance

Session 1. A primer on money creation in a modern economy

Quantitative Easing (QE) and asset valuations. Quantitative Tightening (QT) and capital market turbulence. A view to the future. Long-term refinancing operations, targeted operations, credit easing, outright monetary operations (OMT) and the Covid-19 pandemic emergency programs.

Session 3. Capital Budgeting: Risk, return, and free cas flow analysis

CAPM, asset betas, WACC, and the internal rate of return (IRR) in practice. Data sources: Equity risk premium (ERP), marginal tax rates, sectoral betas and growth rates on operating income (EBIT). Free cash flow analysis: Working capital, sunk costs, tax shields (amortization-depreciation and interest costs). 

Modigliani-Miller irrelevance proposition. An options-based approach to debt and equity valuations. The weighted average cost of capital (WACC) and WACC fallacies. Capital structure under financial frictions. Taxes, financial distress costs and the static trade off (STO) in practice. Debt-overhang: The underinvestment problem and the role of financial restructuring.

Session 2. Capital Structure: Optimal debt-equity choice.

Empirical patterns of corporate financing and possible explanations. Types of financial instruments and markets.

  • Equity capital raising and the mechanics of rights issues.
  •  Incentives, asymmetric information and the pecking-order of financing choices.
Corporate Governance

The object of corporate governance is the control of the proper functioning of businesses in the modern economic environment through the alignment of incentives of executives and other interested parties such as shareholders. With a focus on the separation of management and control, issues such as benefit schemes for business executives, the structure and role of the board of directors, internal and external control mechanisms, alternative corporate governance schemes depending on the nature of the business and the economic environment are examined. is active, as well as issues of corporate social responsibility and sustainability.

Financial Derivative Products (offered by another MSc)

The course covers the main derivative financial products: Forwards and futures on various underlying values. Options on stocks, indices, forex and futures. Interest rate and currency swaps. At the heart of the analysis are models of pricing as well as hedging of risks with derivatives or from derivative positions on behalf of financial organizations. Special topics covered include, among others, the Black-Scholes model, binomial trees, delta hedging as well as various applications such as real rights in finance.

Business Finance and Strategic Business Decisions (offered by another MSc)

The course includes:

Experimental design of alternative products/services. Empirical models of decision making Revealed preference discrete choice data analysis. Stated preference data analysis.

  • After successful completion of the course, students should have a good knowledge of:

    construction of basic models of consumer behavior using experimental design techniques of alternative options/products/services theoretical and practical econometric analysis of simple and advanced choice/preference models. In addition, students are expected to have acquired the relevant skills to: use software and build computing codes independently, collect, process and organize option/market data and extract its characteristics; visualize the data, design consumer behavior surveys isolate and quantify the effect of attributes/factors on consumer choices/preferences.

  • Simulation of options/demand/market shares based on scenarios.
  • Market segmentation, design of preference data collection tools.
Market Regulation and Competition Policy

The course examines business strategies that aim to create or strengthen their market power. It also examines their treatment by Competition Policy, the basic form of regulation of oligopolistic markets. The course combines theory (using examples of oligopolistic competition) and analysis of important cases from Competition Principles in Greece, Europe and the USA. First, unilateral foreclosure strategies by dominant companies are examined. Strategies involving agreements (such as cartels) between firms are then examined. Finally, the horizontal M&A strategy is examined in depth.

Economics of Innovation

The course is interested in issues of definition and consequences of innovations in economics and how these are related to issues of business strategy and market structure. These cover a wide range of economics, including micro, macro, entrepreneurship, licensing, patents and intellectual property, technology and information diffusion, networks and organization, infrastructure, as well as economic policy issues therein.

Quantitative Finance

The course focuses on applied topics in finance using computational and analytical methods. The subjects taught are the following: high-frequency data and realized volatility, the full cross-validation algorithm and basic investment strategy valuation measures (the full backtesting algorithm, arithmetic mean, geometric mean, volatility, Sharpe Ratio, Sortino Ratio, Treynor Ratio, Calmar Ratio, maximum drawdown, alpha, beta, profit/loss Ratio), technical analysis, algorithmic construction of investment strategies based on forecasts of univariate models (cross-sectional momentum, time series momentum, returns signal momentum), construction and portfolio adjustment based on the above methods (re-optimisation and rebalancing), interest rate curve, bond valuation and applications in bond portfolios (analysis, estimation, simulation, applications with derivatives), numerical methods for calculating derivative prices.


3rd Semester


Elaboration of Dissertation

During the period of the 3rd semester, the students of the Master's Specialization Program undertake the writing of their dissertation. Completion of the Diploma is mandatory for full-time program students. The dissertation is supported before the examination committee and its evaluation is based on strict scientific criteria based on originality, depth and analysis, composition and quality.


In order to obtain the Master's of Science Diploma, it is required:

a) The mandatory attendance and successful examination in the courses prescribed by the program as well as the successful examination in the dissertation, where this is required.

b) To have submitted the necessary English certificate as defined in the conditions for admission to the program

c) To have been fulfilled the financial obligations to the Program