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

Courses

Courses

Required Courses Elective Courses
  • Applied Financial Economics I
  • Derivatives Best Practice
  • Applied Financial Economics II
  • Portfolio Management
  • Financial Engineering I
  • Financial Engineering II
  • Applied Financial Econometrics I
  • Applied Financial Econometrics II

Financial Risk Management (FRM) Electives:

  • Applied Asset and Liability Management 1
  • Applied Asset and Liability Management 2
  • Demystifying Turkish Economic Data
  • Financial Ethics
  • Financial Markets, Institutions, and Instruments
  • Financial Modeling and Business Valuation
  • Fixed Income in Emerging Markets
  • Macroeconomic Projections and Scenario Analysis
  • Quantitative Asset Management
  • Quantitative Trading
  • Risk Management in Financial Institutions

Financial Data Analytics (FDA) Electives:

  • Big Data Management
  • Customer Analytics
  • Data Analytics and Statistical Learning
  • Financial Technologies (FinTech)
  • Introduction to Financial Economics and Statistics
  • Machine Learning and Deep Learning
  • R for Financial Data Analytics

COURSE DESCRIPTIONS:

Required Courses:

FERM 501-Applied Financial Economics I:This course provides a rigorous introduction to the fundamentals of modern financial analysis and their applications to business challenges in capital budgeting, project evaluation, corporate investment and financing decisions, and basic security analysis and investment management.

FERM 502-Derivatives Best Practice: This course introduces the analysis of financial derivatives and their use in risk management. It starts with the pricing of futures and options contracts in derivatives markets and the no-arbitrage conditions that play an essential role in this process, and then continues with the use of these instruments for hedging and speculative purposes, complex positions that can be constructed using these instruments, and the risks such positions entail.

FERM 503-Applied Financial Economics II: This course provides an advanced knowledge of finance theory to make good investment decisions, to understand the paradigm of security, individual decision-making, risk and uncertainty, arbitrage, and market equilibrium.

FERM 504-Portfolio Management: The course concentrates on finance theory and empirical analysis that is essential for investing in public markets. Topics covered include measurement of risk and return, capital allocation, modern portfolio theory, asset pricing models, market efficiency, and fund performance evaluation.

FERM 505-Financial Engineering I: This class is about mathematical models to price financial derivative securities. It focuses on how to use these models to hedge the risk exposures of financial portfolios.

FERM 506-Financial Engineering II: The aim of this course is to provide an introduction to the mathematical modelling of financial markets with particular emphasis on the pricing of derivative securities and the management of risk. First, the necessary mathematical background is established by introducing Brownian Motion, Stochastic Calculus and Martingale Theory. Afterwards, financial applications of the theory built are executed on the following specific topics: pricing of general European derivative securities, pricing American Options, hedging portfolios via options, pricing Exotic Options.

FERM 533-Applied Financial Econometrics I: Decision-making in business and finance is often based on quantitative data analysis. To perform such data analysis, one needs to understand the role of uncertainty and the ways to describe it. This module provides the foundations in probability and statistics necessary to understand and build empirical financial data models. The weekly meetings consist of discussions of the conceptual framework and detailed examples, as well as R-related mini workshops where the studied concepts are implemented in practice. This provides support for the empirical applications in FERM 534 Applied Financial Econometrics II module, as well as the rest of the empirically-oriented modules in the FERM program.

FERM 534-Applied Financial Econometrics II:This course provides the foundations of empirical financial time-series data modeling, directly applicable to areas such as investing and portfolio management, as well as financial risk analysis. It addresses the basic characteristics of financial data, the relationships among financial and economic variables, and the application of financial econometric models. In addition, it discusses the ethical considerations in analyzing and modeling financial data, and presenting the results of financial models. The weekly meetings consist of discussions of the conceptual framework, detailed examples and applications, as well as Matlab-based mini workshops.

Elective Courses

Financial Risk Management (FRM) Electives:

FERM 519-Applied Asset and Liability Management 1: In the first series of this course, the ALM simulation tool is introduced. ALM actions which give rise to liquidity crisis, foreign currency and interest rate losses are discussed using predefined scenarios in the simulation. Sound liquidity management and foreign currency position management actions and their affects are discussed using the simulation. Interest rate instruments and their use in interest rate management are explored.

FERM 520-Applied Asset and Liability Management 2: In this course, students act as head of Asset and Liability Management (ALM) of a bank and take decisions in a randomly changing environment using the simulation tool. Results of their decisions are discussed throughout the lectures while more advanced topics like pitfalls in current risk models, dynamics of mortgage and credit card loans are covered.

FERM 536-Demystifying Turkish Economic Data: Each week, the basic characteristics of data sets for a different section of the Turkish economy will be discussed. These data sets will be analyzed by appropriate statistical methods. The sections of the Turkish economy will provide a wide spectrum including monetary policy, financial markets, fiscal policy, foreign trade dynamics, labor market etc.

FERM 525-Financial Ethics: The course starts with exploring theories of rational ethics and current best practice. The students discuss general cases involving ethical dilemmas that professionals encounter when making financial decisions. The course also features several real-life examples of ethical issues in case studies. In these studies, student apply their theoretical knowledge of ethical principles and develop solutions to resolve these dilemmas. Group discussions among students support them to share insights and develop a deeper understanding of ethical principles.

FERM 513-Financial Markets, Institutions, and Instruments: This course introduces how the institutions in capital markets work and how trading & clearing takes place in capital markets. It starts with the analysis of financial institutions on the regulatory and practical basis with the understanding of their functions, relations and roles. The course also includes details of how the Turkish and Global Financial Markets work. Trading and clearing are main topics in the lectures. Risk management in financial markets like hedging are also investigated. Professionals from Capital markets contribute to the lecture with their experiences and practices.

FERM 511-Financial Modeling and Business Valuation: The course concentrates on fundamental principles of business valuation models. Topics covered include financial statement analysis, discounted cash flow analysis and multiple comparables. Financials of a listed company are integrated into an Excel based valuation model to derive its intrinsic value.

FERM 515-Fixed Income in Emerging Markets:The course addresses analytical tools to value interest rate sensitive cashflows and how to search for arbitrage strategies among various types of fixed income instruments. The course covers vanilla bonds, bonds with embedded options, corporate bonds, mortgage-backed securities like Collateralized Mortgage Obligations and derivative securities like swaps, caps, floors, and swaptions. Hedging strategies based on yields and forward rates, duration, convexity, and factor models of yield curve dynamics are also explored.

FERM 538-Macroeconomic Projections and Scenario Analysis: The course has three pillars: the first is a sufficient theoretical background of macroeconomic fundamentals, the second is a solid perspective of data analysis and the third is a holistic treatment of issues within spreadsheets, i.e. in the absence of more sophisticated analytical tools.

FERM 526-Quantitative Asset Management: This course offers a strong, well-rounded, and an in-depth understanding of investments and financial markets. It builds on techniques and skills that were covered in courses that evaluate and understand financial markets. The course evaluates market efficiency, anomalies, predictable price patterns, mutual fund and hedge fund strategies. It covers advanced theoretical concepts, but the primary focus is on real-world applications through case studies, projects, and class discussions.

FERM 527-Quantitative Trading: The course reviews efficient markets hypothesis and market predictability, explains speculation theory and basics of behavioral finance, analyzes trading mechanisms such as momentum and pairs trading.

FERM 523-Risk Management in Financial Institutions: This course focuses on integrated risk management in financial institutions. Students acquire analytical and practical tools to manage market risk, liquidity risk, and counterparty risk. Main tools covered in this course include VaR, Scenario Analysis, Sensitivity Tests, Stress Tests and Back testing of Models.

Financial Data Analytics (FDA) Electives:

FERM 509-Big Data Management: This course discusses selected topics on big data management, including Hadoop and MapReduce, data models, relational databases and SQL, data stream, data lake and data integration. Students gain hands-on experience for different systems, including Hadoop, PostgreSQL and MongoDB. The course is an introductory course and most of the above topics and systems are introduced with a high-level description

FERM 514-Customer Analytics: The goal of customer analytics is to create a single, accurate view of a customer to make decisions about how best to acquire and retain customers, identify high-value customers and interact with them. To achieve these objectives, the course teaches data collection and model building techniques for customer analytics. The weekly meetings cover key concepts in Customer Analytics using detailed examples and case studies

FERM 524-Data Analytics and Statistical Learning: This course provides a theoretical foundation in basic and advanced data analytics, and extracts useful patterns and predictions from real-life financial data using statistical software programs. All topics are covered within the framework of Supervised and Unsupervised Machine Learning methodologies.

FERM 539-Financial Technologies (FinTech): This course focuses on the technological advancements, innovative trends and startups that are shaping the Financial Services Industry. The course introduces both the technologies and the firms (both startups and big corporations) that are using these technologies for innovative solutions. The following topics are covered: The Future of Financial Services Industry; The Effects of Financial Technologies on Innovation and New Business Models; The Opportunities and Threats of Financial Technologies; The FinTech Ecosystem; Technological Trends and Use Cases; Open Banking and API Economy; Blockchain Technology; FinTech Regulations; FinTech Startups and Business Models; The Strategic Partnership Models Between Corporations and Startups

FERM 530-Introduction to Financial Economics and Statistics: This course provides a summary of the foundations in financial economics, probability, statistics necessary to understand financial decision making and risk management. The weekly meetings consist of discussions of the conceptual framework and detailed examples.

FERM 508-Machine Learning and Deep Learning: Decision-making in finance is based on quantitative data analysis. To perform such analysis, this course goes beyond the traditional econometric and statistical models and explores Machine Learning and Deep Learning models. The weekly meetings cover key concepts and detailed examples and case studies in Machine Learning and Deep Learning models.

FERM 540-R for Financial Data Analytics: Decision-making in finance is based on quantitative data analysis. To perform such data analysis, this course uses the R statistical language. The weekly meetings cover R programming language concepts and detailed examples to achieve the course outcomes. This course will provide support for the empirical applications in FERM 533 Applied Financial Econometrics I and FERM 534 Applied Financial Econometrics II courses that are administered in R language as well.

Graduation Project:

PROJ 580-Graduation Project: To guide the students in coming up with and evaluating innovative financial engineering practices.