
Duration of studies: 24 months

Awarded degree: Professional Master in CS

Qualification: Quant Developer

Language of studies: English

Form of studies: 100% online (on the basis of the Moodle platform)
Master in Quantitative Enterprise Risk Management is the ONLY global class professional qualification and ACADEMIC CREDENTIAL which sets the NEW STANDARD in the field of financial engineering. It satisfies quickly growing demand to be effectively delivering risk management solutions within financial organizations in the constantly evolving international regulatory environment and continually improving patented risk mitigation strategies.
The Program is one of a kind dedicated master’s program in quantitative risk management, which has been built and is being delivered by world top industry practitioners in the US and EU. It facilitates work visas for internships and work contracts to best students and graduates.
Today’s sources of talent, both professional and academic, are unable to supply the sufficient number of resources with the necessary skills to deliver the above solutions.
I. Specialization Structure
The course of study is divided into 4 modules consisting of 14 classes during 5 academic semesters:
 Foundation Math
 Scientific calculations, fundamentals of statistical training and data analysis
 Fundamentals of financial engineering, financial markets and products
 Enterprise risk management: market, credit and model risks
Each course includes numerous practical assignments, quizzes, verbal defences/presentations, and examinations. The curriculum is constantly updated to reflect the latest developments in the global regulatory space and risk management methodology.
II. Lecturers
The Program is implemented by a group of Adjunct Professors, who are at the same time top level practitioners leading teams of quantitative developers within such global financial institutions as Citigroup, Chase, Bank of America, KPMG, Bloomberg, State Street, CantorFitzgerald, and others. The really elite faculty will provide very uptodate practical content. All the instructors are personally interested in providing the highest quality training, since they are actually the potential employers for successful students and graduates.
III. Admissions
Each applicant shall undergo rigorous selection process, which shall include academic and professional testing, interviews, recommendations, etc.
IV. Target Candidate Group
Young specialists with academic background in mathematics, physics, or computer science with 1 or more years of professional experience utilizing quantitative and programming skills
V. Employment
The focus of the Curriculum and close bond between the Program and the industry, practically ensures students and graduates the employment with the world leading financial institutions. During the course of study, each student’s mastery level shall be continuously assessed with the aim of most expedited participation in the real projects, ultimately even before the Program completion. To facilitate placement with international financial companies, multiyear working visas outside of CIS and Baltic countries shall be made available to best students and graduates. Work visas shall make it possible for potential employers to effectively eliminate the lengthy engagement process for international candidates and to complete the onboarding quickly, usually within one month.
VI. To Enrol:
 Please submit 2 letters of recommendation from your latest school's Program Director and Dean to the Program's Admissions' Committee (PAC) with their contact information
 Please submit a letter of recommendation to PAC from your latest place of employment (if available) from your immediate manager with his/her contact information
 Please submit your latest academic transcript to PAC
 Please write a 2page autobiographical essay in English and send it to PAC  1 hour timed and proctored exercise
 Hold a 1hourlong discussion about your essay with PAC (all in English)
VII. Admission Test
Threehour timed and proctored written exercise in English:
 Math requirements (1.5 hours)
 Integral and differential calculus (univariate is acceptable) – 2 questions
 Linear algebra – 2 questions
 Theory of complex variables (don’t have to know complex analysis) – 2 questions
 Basic Probability Theory – 2 questions
 Fundamentals of Numerical Methods (for instance, basic numerical differentiation and integration, root  finding, etc.) – 2 questions
 Programming skills (1.5 hours)
 Excel (VBA is a plus) – 2 questions
 High level programming language (C++/python preferred)
 Algorithms and data structures – 2 questions
 Performance optimization – 2 questions
 Parallel computing – 2 questions
 Basics of numerical computations (with finite precision floats) – 2 questions
VIII. Specialization testing verbal defence – going over the written test results and discussion (2 hours)
More information about enrolment: risk.management@isma.lv
STUDY PLAN – 5 ACADEMIC SEMESTERS
Introduction to math, programming and finance
The prerequisite course to introduce students to the fundamentals of mathematics, programming, and finance:Broad course to prepare students for advanced study of financial mathematics and programming covering but not limited to differential equations, linear alegebra, probability theory, C++ and Python object oriented programming, as well as basics of finance theory.
Foundation Math I (1)
The Foundation Math I Course focuses on algebraic and numerical skills in a context of applications and problem solving to prepare students for the study of Financial Mathematics in the context of Risk Management.
Upon completing this course, students will be capable of:

Applying of derivatives and integrals, transcendental functions, methods of integration, integration applications, sequences, infinite series, Taylor's theorem

Solving systems of linear equations, matrix operations including inverses, linear dependence, vector spaces, least square problems, determinants and their properties, eigenvalues and eigenvectors, linear transformations, matrix decompositions

Using functions of several variables, limits and continuity, partial derivatives, linearization, Jacobians, higher derivatives, Taylor theorem in many dimensions

Using discrete and continuous random variables, expectations, probability distributions, laws of large numbers, elements of Bayesian inference, random processes

Working with complex numbers – representation and algebraic operations, analytical functions, CauchyRiemann equations, integration in the complex plane, series representation
Foundation Math II (2)
The Foundation Math II Course builds up on the basic concepts, presented in the Foundation Math I Course and prepares students to use following in application to certain risk related problems:

Theory of boundary value and initial value problems with emphasis on linear equations, heat equation, wave equation, Laplace equation, SturmLiouville theory, Greens functions, Laplace transform

Basics of Fourier series, Fourier integrals, and orthogonal sets of functions, applications of Fourier analysis to fields such as approximation theory, signal analysis, probability, statistics, and differential equations

Standard algorithms for numerical computations including root finding, interpolation and approximation of functions, numerical differentiation and integration, numerical solutions of ordinary differential equations and boundary value problems

Algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control including the simplex method, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method
Foundation Math III – Stochastic Calculus (3)
The Foundation Math III – Stochastic Calculus Course is designed to teach students the basics of Stochastic Calculus enabling them to do valuations of financial derivatives. It is intended as an accessible introduction with the goal of clearly distinguishing the mathematics that is convenient for a first introduction, and the more rigorous underpinnings which are best studied from the selected technical references. Standard probability theory and ordinary calculus are the prerequisites for taking this course.
Upon completing the course, the students will know and be comfortable of applying the following concepts:

Brownian Motion specification and construction, correlated Brownian Motions, features of the Brownian Motion path

Martingales and their properties, conditional expectations, elements of martingale analysis

Ito stochastic integral, Ito calculus, Ito’s lemma

Stochastic differential equations, arithmetic Brownian motion, geometric Brownian Motion, mean reverting and OU SDEs

Change of probability, Girsanov transformation, change in numeraire in discrete and continuous time, application to geometric Brownian Motion
Python for Scientific Computing (4)
The goal of this course is to aid both theoretical and data driven understanding of economic systems with the help of computer programming. The purpose of this course is to equip the students with a powerful programming language called Python and to introduce them to the realm of simulationbased understanding of dynamical processes like autoregression or random walk.
Upon completing the course, the students will be capable of applying the following concepts in the risk space:

Basic Python: data types, operators, variables

Branching, conditionals, iteration

Abstraction through functions, recursion

Bisection methods to find root, binary search, sorting

Lists, NumPy arrays, tuples, mutability, dictionary  introduction to efficiency

Testing and debugging

Complexity: log, linear, quadratic, exponential

Dynamic programming: Fibonacci, knapsack (continuous vs integral), greedy approaches  why are they suboptimal

Scientific libraries: NumPy, SciPy, Matplotlib

Object oriented programming

Simulation: random walk
Introduction to Statistical Learning with Python and R (5)
This course is a survey of statistical learning methods and will cover major techniques and concepts for both supervised and unsupervised learning. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to apply basic statistical learning methods to build predictive models or perform exploratory analysis, properly tune, select, and validate statistical learning models, and build an ensemble of learning algorithms.
Topics covered will include:

Population vs sample, statistical measures, confidence interval estimation, hypothesis testing

Simple linear regression models, evaluating regression models, multiple linear regression, outliers and influential data points and how to deal with these, estimation beyond ordinary least squares

Linear classifiers, SVM, kernel estimation, knearest neighbour, neural networks

Bootstrap resampling, crossvalidation, bootstrap aggregation (bagging), accuracy and precision, reliability
Introduction to Data Science (6)
This course will introduce students to the rapidly growing field of Data Science and equip them with some of its basic principles and tools as well as its general mindset.Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modelling, descriptive modelling, data product creation, evaluation, and effective communication.The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used.
Topics covered include:

Introduction to Data Science Tools: SciKitLearn, TensorFlow, Teano, Keras

Algorithms for supervised and unsupervised learning: kNN, Naïve Bayes, Logistic Regression, Decision Trees, Random Forests, etc.

Feature selection algorithms: filter methods, wrapper methods, embedded methods

Techniques and methodologies for evaluating and tuning the models
Introduction to Math Finance (7)
This course is an introduction to the mathematical modelling of financial markets with particular emphasis on pricing derivative securities and management of risk.
Topics covered include:

Random behaviour of assets: probabilistic model for the movement of asset returns

Binomial Model: introduction to options pricing

BlackScholes Model: fundamentals of BlackScholesMerton framework for options pricing

Greeks and Delta Hedging: risk sensitivities in BSM framework, calculation methods, application

Volatility Modelling: the concept of volatility, modelling fundamentals
Products and Markets I – Equities, Commodities and FX (8)
The aim of this course is to give students the necessary knowledge of Equities, Commodities and FX Markets and present a broad overview of the products traded.
Upon completing this course the students will be comfortable working with:

Equities and Equity Derivatives

FX and FX Derivatives

Commodity and Commodity Derivatives

Models and methods for Valuation of Vanilla Products

Models and methods for Valuation of Exotic and PathDependent products

Fundamentals of Equities, Commodities and FX Volatility Modelling

Basic Principles of Risk Management for Equities, Commodities and FX
Products and Markets II – Rates and Credit (9)
The aim of this course is to provide the students with Interest Rates and Credit Markets’ knowledge and to present a broad overview of the products traded.
Upon completing this course the students will possess solid knowledge of:

Rates Markets, Swaps and Derivatives

Yield Curve Estimation in Modern Markets

Interest Rate Models

Interest Rate Volatility Modelling in Modern Markets

Valuation of Vanilla Products and Exotic Interest Rate Derivatives

Credit Markets, Vanilla Instruments (CDS/CDX), CDX Options and Correlation Trading

Structural and Reduced Form credit models

Fundamentals of Risk Management for Interest Rate and Credit Products
Model Risk (10)
This course is designed to introduce the students to a relatively new concept of model risk and to raise awareness of the potential pitfalls of the Derivatives Valuation and Risk Management tasks. Upon completion of this course the students will understand the nature of model risk, be aware of the damage that could be inflicted by irresponsible modelling and develop sufficient understanding of the methods and techniques necessary for managing model risk.
The course will cover:

Model Assumptions and Postcrisis Marketplace: negative interest rates and rate basis, dangers of correlation modelling, etc.

Quality of Approximations: impact on valuations and risk, assessment of

Model Calibration: common pitfalls, impact on valuation and risk, assessment of quality

Methodologies for Stress Testing and Backtesting
Market Risk (11)
Market Risk Management course is designed to provide participants with a firm grasp of the fundamentals of market risk and introduce them to the basic market risk management techniques. Besides addressing the traditional approach in measuring and managing market risk, this course will focus on modern risk measures and techniques, especially those that arose recently in connection with the latest regulatory initiatives, such as the Fundamental Review of the Trading Book (FRTB).
Topics covered will include:

Value at Risk and Other Risk Metrics: relative merits of different risk metrics, the concept of coherent measures of risk

Parametric Linear VaR Models

Historical Simulation Method

MonteCarlo Methods

Scenario Analysis and Stress Testing
Counterparty Credit Risk I (12)
This course will introduce the students to the concept of Counterparty Credit Risk, explain the sources and nature of the counterparty credit exposure, discuss the quantification of such exposure in different contexts and outline basic risk mitigation methods. The course content is constantly updated to include the latest industry advances in measuring and managing the counterparty credit exposure.
Topics covered within this course will include:

The concept of CVA and Credit Limits

Methods for Hedging Counterparty Risk

Risk Mitigation with Netting, Close Out and Collateral

Credit Exposure and Funding

Introduction to the concept of Capital Requirements

The function of Clearing and Central Counterparties

The concept of Wrong Way Risk
Counterparty Credit Risk II (13)
This course will train students on the advanced methods for Counterparty Credit Exposure measurement and management of the Counterparty Credit Risk. Special focus is given to the Internal Model Method for Counterparty Credit Exposure calculations and the development of the historical measure scenario generation models for long term portfolio simulation.
Topics covered include:

Quantifying Credit Exposure – Introduction to xVA

xVA Management and Optimization

Risk Neutral Vs Real Measure Models

Modelling in Real World Measure

Calibration of Real World Measure Models
Risk Capital Optimization (14)
This course is an indepth treatment of the Basel III Risk Management Standard. Upon completion of this course, the students will have solid grasp on the concept of Regulatory Capital, have good understanding of the fundamental methods for calculating the Basel III Counterparty Credit Exposure and respective capital charges, and be capable of applying the advanced methods for reducing Basel III Credit Capital.
Charges via the application of Internal Model Method. Topics will include:

Introduction to Risk Capital, Overview of the Latest Regulations

Constituents of Risk Capital

Quantifying Risk Capital – General Methodology

Quantifying Credit, Market and Liquidity Capital Charges – Standardized vs Advanced approach

Optimizing Risk Capital – Internal Models Method