Program Structure

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.

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.

Instructor: Tzavalis Elias

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.

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.

Instructor: Vrontos Ioannis

Computational Econometrics for Economics 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).

Instructor: Dendramis Yiannis

Data Analytics for Applied Macroeconomics and Finance

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.

Instructor: Papailias Fotios

Indicative list of elective courses

Behavioral Economics

COURSE CODE : m12220f

LEARNING OUTCOMES :

The objective of the course after its completion is:

• Students must have understood  behavioural game theory, design of economic experiments, analysis of consumer behavior at the micro and macro level and analytical methods for understanding and predicting individual and social behavior
• Also, students will get the analytical tools of applying the aforementioned learning outcomes on economics, business, finance and marketing .

SYLLABUS: 

This course examines the role of systematic bias in the financial decisions of businesses and individuals, such as pricing and consumption decisions. The analysis is done first on a theoretical level through the construction of models and then through the construction of experiments and econometric analyzes. More specifically, the course focuses on biases and economic preferences such as overconfidence, aversion to loss, aversion to uncertainty, reciprocity,  the attachment to reference points, the pursuit of social status and the cultural dimensions of human behaviour. Finally, we will develop analytical and quantitative behavioural tools to explain the behaviour of microeconomic and macroeconomic variables and we will extend the analysis to different subfields such as behavioral industrial organization, marketing, economic policy, finance and business

Instructor: Dioikitopoulos Evangelos

Python for Business Economics and Finance

LEARNING OUTCOMES :

The objective of the course is students after its completion:

• Demonstrate proficiency in fundamental Python programming concepts, including data structures, control flow, and functions to be able to quantify
• Apply Python to solve practical problems in business, economics, and finance by developing financial models, conducting economic forecasting, and performing data-driven decision-making
• Analyze financial datasets and make data-driven business decisions using both traditional and modern data analytics methods.

The acquisition of the following skills:

Utilize Python’s key libraries such as Pandas, NumPy, Matplotlib, and Statsmodels to manipulate, analyze, and visualize data. Effectively communicate analytical results through data visualizations and reports, enhancing decision-making capabilities in a business or financial context.

SYLLABUS :   

This course introduces students to Python programming with a focus on its application in business, economics, and finance. Designed for beginners with very limited prior coding experience, the course covers essential Python concepts, including data structures, control flow, and functions. Students will learn how to leverage Python for data analysis, financial modeling, and economic forecasting. Through hands-on projects and real-world case studies, students will explore topics such as forecasting, risk management, and business decision-making by using traditional as well as modern data analytics methods. Key libraries like Pandas, NumPy, Matplotlib, and Statsmodels will be used to manipulate financial datasets, visualize trends, and build predictive models. By the end of the course, students will be able to confidently apply Python programming to solve practical problems in business economics and finance, improving their analytical skills and decision-making capabilities.

Instructor: Alexopoulos Angelos

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

COURSE CODE : m44110f

LEARNING OUTCOMES: 

Corporate Finance is one of the seven core courses of the program. Students taking this course should be able to:

1. Identify turning points in economic policy that could have a material impact on funding conditions and corporate decisions to access external financing
2. Navigate in the new era of extraordinary policy interventions by central banks that have a profound impact on asset valuations and the cost of corporate financing.
3. Value investment projects, conduct capital budgeting exercises, and identify factors that affect corporate decisions to access different forms of financing.
4. Assess alternative ways of accessing capital markets.
5. Identify issues of first-order importance that are relevant to corporate financing, combine them to make informed decisions and negotiate funding terms with financiers.

SYLLABUS :   

This course examines how firms access external funding and factors that affect their capital structure decisions. It also covers topics on investment valuation and capital budgeting.

The first part of the course (Section 1) focuses on financial statements, key financial indicators, capital budgeting and investment appraisal. Topical issues are discussed, and special topics are covered, such as debt amortization and stock valuation. Students are also equipped with the knowledge to apply asset valuation in practice using appropriate data sources.

The second part of the course (Section 2) covers capital budgeting and business plans. A case study is discussed extensively in class. Students are then asked to prepare, submit, and present their own financial analysis of a chosen firm, and participate in a simulated exercise in class representing the firm in negotiations with external investors about terms of funding.

The third part of the course (Section 3) focuses on the micro-foundations of corporate financing. Using Modigliani-Miller (MM) irrelevance proposition as a guiding benchmark, students analyse the impact of real-world financial decisions, such as dividend policy and stock buybacks, on equity valuation and the cost of funding. Topical issues are covered, such as stock issuance using pre-emption rights, and renegotiations of legacy debt. Moreover, real-world examples are considered where capital markets are subject to distortions and frictions, such as financial distress costs, principle-agent problems, and asymmetric information.  

Overall, students are equipped with the analytical apparatus to identify first-order issues relevant to corporate financing decisions and learn how to combine and apply them in practice.  

Instructor: Pagratis Spyridon

The Macroeconomics of Financial Markets

LEARNING OUTCOMES :

The objective of the course is students after its completion:

• to have further theoretical and computational knowledge of financial markets
• to be able to describe and use basic macroeconomic models to analyze financial data
• to be able to explain the basic puzzles of financial markets
• to be able to explain the importance and the effects of financial imperfections on the macroeconomy

The acquisition of the following skills: analysis and presentation of financial data, solving macroeconomic models with Matlab (Dynare) and Python

SYLLABUS :   This course begins where the Market Analysis and Portfolio Management course stopped. It will offer a computational and modeling perspective in Macro-Finance with a strong emphasis on comparing models with empirical data. It is directed to students aiming at careers in banking, as financial analysts/quants, in economic policy (central banks, policy institutes), as well students interested in doing research in macroeconomics or finance. We will cover the following:

0) Macro and Finance: Why study them together?

1) Review of portfolio management basics (choice under uncertainty, CAPM)

2) Complete markets and standard asset pricing (stochastic discount factor, perfect risk sharing, Lucas trees, Q theory of investment, Modigliani-Miller)

3) Asset pricing puzzles (equity premium puzzle, risk premium puzzle and resolutions)

4) Incomplete markets, heterogeneous agents, and precautionary savings

5) Financial frictions (Kiyotaki-Moore, Costly-State verification, implications for investment)

6) Search Models of over the counter markets (Duffie, Garleanu, Pedersen) if time permitts

There will 3-4 assignments with model solving in Matlab (Dynare) or Python

Instructor: Kospentaris Ioannis

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.

Data Analytics for Applied Microeconomics and Business Strategy 

COURSE CODE : m13219f

LEARNING OUTCOMES :

The objective of the course is students after its completion:

After successful completion of this course the students must have a good understanding of:

• experimental design theory and applications
• basic preference/choice model building
• theory and econometric estimation of basic and advanced choice/preference models

Furthermore, students are expected to obtain the necessary skills to :

• use scientific software and develop codes independently,
• collect, handle and organize panels of choice data,
• visualize data and extract features,
• decompose and quantify the effect of attributes/characteristics on consumers’ choices/preferences
• simulate and predict choices, demand and market shares
• design data collection tools for the estimation of behavioral models 

SYLLABUS :   

1. Introduction to structural models of decision-making
2. Basic methods for estimating structural parameters
3. Revealed preference discrete choice microdata analysis
4. Stated preference microdata analysis
5. Simulation of options/demand/market shares based on scenarios
6. Preference-based market segmentation
7. Experimental design of alternative products/services for microdata collection
8. Demand estimation in the characteristic space using structural microeconomic models

Instructor: Vassilopoulos Achilleas

International Finance and Portfolio Management (offered by another MSc)

LEARNING OUTCOMES:

The objective of the course is students after its completion:

• to acquire skills in modern theoretical and applied tools for understanding international financial crises and to gain knowledge in international markets and economic relations, as well as in portfolio management of stocks under exchange rate risk. 
• To acquire analytical and technical skills to assess exchange rate risk and other investment risks in the international environment, as well as currency speculative attacks.
• Be capable of understanding how economic policy (monetary/exchange rate and/or fiscal) affects an economy in the international financial system, both during normal and crises periods.
• To acquire modern knowledge, skills, and tools used in international investment institutions (e.g., private banks and investment firms), as well as in international organizations and economic policy institutes (e.g., central banks, OECD, International Monetary Fund).

The acquisition of the following skills: Analytical methods and computational tools (e.g., Python) for processing macroeconomic and financial data, applying them to understand the transmission mechanisms of economic crises, and analyzing economic policies to address such crises. Additionally, the ability to forecast exchange rate changes, manage portfolios with foreign stocks and exchange rate risk, and evaluate stock prices and returns in an international environment.

SYLLABUS:   

    This course is designed for students aspiring to build international careers in financial and policy institutions, such as private and investment banks, central banks, and other economic policy institutions. It provides a comprehensive introduction to international and macro finance, with a particular emphasis on money, banking, and the macroeconomic forces that influence financial markets and exchange rate fluctuations.

    Throughout the course, students will examine key case studies of major international macroeconomic and financial crises, including the Global Financial Crisis (2008-09), the Irish Banking Crisis (2009-10), and the European Debt Crisis (2010). These examples offer a foundation for understanding the complex dynamics behind financial turmoil. In addition, students will learn to apply analytical and technical tools to process macroeconomic and financial data before, during, and after such crises.

    Having acquired the necessary tools to understand financial and macroeconomic crises, next the course shifts focus to practical techniques for managing stock portfolios in an international context. Students will explore strategies for mitigating exchange rate risk and managing globally diversified portfolios. The course will also introduce models that assess stock market risk in the presence of exchange rate volatility. These models are applied to international investment decisions and are adapted to scenarios where traditional exchange rate theories, such as purchasing power parity or monetary theory, may not hold.

Instructors: Tzavalis Elias and Varthalitis Petros

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.

Special Issues in Financial Economics

COURSE CODE : m13216f

LEARNING OUTCOMES :

The objective of the course is students after its completion:

• to extend their knowledge to other areas of of International Finance and exchange risk hedging.
• to be able to quantify risks and construct international portfolios for better exchange rate risk management.
• to be able to appraise exchange rate risk in the context of capital budgets and international investments.
• to be able to hedge this risk with financial derivatives

The acquisition of the following skills:

• Practical knowledge of portfolio management with foreign stocks and bonds
• Application of currency risk assessment models
• Application of real options
• Management of international portfolios, and hedging of currency risks using derivatives
• Application of measures to evaluate and compare the performance of international asset portfolios and VaR measurement techniques

SYLLABUS:

The course covers the following topics:

1. International portfolios and risk management using derivatives
2. Portfolio performance evaluation and active portfolio management techniques
3. Pricing of exchange rate risk
4. International investments
5. Measuring risk with metrics VaR and CvaR and the use of computers

Instructor:  Tzavalis Elias

Financial Intermediation and Monetary Economics

COURSE CODE : m12218f

LEARNING OUTCOMES :

The objective of the course is students after its completion:

• to extend their knowledge to the areas of monetary economics and financial markets
• to be able to understand the implications of specific economic policies on economy’s liquidity as well as on the functioning of financial markets

The main objective of the course is for students to understand the relationship between the banking system and the financial markets. It focuses on how liquidity is formed in an economy, and on the catalytic role that the commercial banking system has in this process. The determinants of the money supply, the tools of monetary policy and the role of the Central Bank are presented. The money demand side is analyzed and monetary policy transmission mechanisms are presented. Special reference is made to the ECB and the Eurosystem

SYLLABUS :

• Why do we study money markets and the functioning of the banking system? The financial system. Money.
• Creating liquidity through deposits and money supply. Determinants of the money supply. Monetary policy tools. What is the role of the Central Bank? Monetary policy, objectives, strategy.
• Demand for money. The IS-LM model. Monetary and fiscal policy in the IS-LM model. Aggregate demand and supply.
• Monetary policy transmission mechanisms.
• Money and inflation.
• Rational expectations: implications for policy.
• The international financial markets. The international financial system.

.Instructors: Pagratis Spyridon and Economides George

Market Microstructure with Computational and Statistical Methods

COURSE CODE : m12222f

LEARNING OUTCOMES :

Market Microstructure is the field that deals with the organization of markets and their participants. Specifically, the dynamics of trade and price developments in different markets are examined by studying:

the rules governing trading. the types of market-participants. their incentives, and the strategies they choose to achieve their objectives.

Instructor: (TBA)

The course covers the following topics:

Market Industry: Buy/Sell side, dealers, brokers, clearing and settlement. Orders, Algos and algorithmic trading. The role of dealers, brokers. Main categories of market-users (profit-motivated, utilitarian, noise traders) and their incentives. Basic strategies of each of these categories and how they affect the market mechanism. Price discovery in exchanges and OTC markets. The incorporation of information in market prices and the informational content of trades. Market structures: Order-driven, Dealer-to-Customer, Crossing-networks and hybrid markets. The nature of liquidity and volatility, their relationship and how they both affect market efficiency. Manifestations of asymmetric information, strategies for exploiting the information advantage and ways of protection against the risk arising from it. Key microstructure models: Garman, Roll, Glosten-Millgrom, Easley- O’Hara, Kyle, Glosten-Harris. Empirical Portfolio Analysis and trading strategies, with the use of software including Microsoft Office Excel and Python.

The student will be able to interpret the very short-term market dynamics, as well as to assess the possible strategic decisions that traders face over the course of a day. The syllabus covers both theoretical work and empirical work

SYLLABUS:

The course covers the following topics:

Market Industry: Buy/Sell side, dealers, brokers, clearing and settlement. Orders, Algos and algorithmic trading.

Instructor: (TBA)

• The role of dealers, brokers.
• Main categories of market-users (profit-motivated, utilitarian, noise traders) and their incentives.
• Basic strategies of each of these categories and how they affect the market mechanism.
• Price discovery in exchanges and OTC markets.
• The incorporation of information in market prices and the informational content of trades.
• Market structures: Order-driven, Dealer-to-Customer, Crossing-networks and hybrid markets.
• The nature of liquidity and volatility, their relationship and how they both affect market efficiency.
• Manifestations of asymmetric information, strategies for exploiting the information advantage and ways of protection against the risk arising from it.
• Key microstructure models: Garman, Roll, Glosten-Millgrom, Easley- O’Hara, Kyle, Glosten-Harris.
• Empirical Portfolio Analysis and trading strategies, with the use of software including Microsoft Office Excel and Python.

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.