The recent global economic and financial crisis has once again revealed to us the crucial importance of understanding the intricacies of risk analysis and risk management applied to finance. This is equally true for private sector corporate actors, public sector officials and professionals working in private financial institutions and in public administrations such as ministries of finance and central banks. Measuring risk using mathematical formulae has become standard for major financial institutions worldwide, and the cost of borrowing or leveraging debt depends to a large extent upon the findings of risk and creditworthiness found through mathematical modeling and applying sophisticated statistical tools. This advanced course is designed to provide professionals with specific hands-on tools and modeling techniques for effective risk management, and builds on the Fundamentals of Risk Management course.
At the end of the course, the participants should be able to:
- Apply the mathematical, statistical and financial tools required to approach financial risk management;
- Compute the value of different types of fixed income securities, including calculations on yields and market prices;
- Interpret different ratios and indexes related to the stock exchange market, in particular for stock shares;
- Apply risk management techniques into different situations, in particular Value at Risk (VaR);
- Calculate risk indexes for a portfolio composed of different types of securities;
- Design different methodologies for generating risk management scenarios, in particular for interest rate or exchange rate variations, using Monte Carlo Simulation; and
- Evaluate methodologies applied by third parties on financial risk management.
The course consists of the following modules:
- Module 1: Review of Basic Knowledge: Statistics, Basic Matrix Algebra Operation and Mathematics of Finance
- Module 2: Traded Instruments and Risk Indicators
- Module 3: Basic Concerns of Risk Management and Corresponding Tools
- Module 4: Tools and Strategies for Decreasing Risk
In order to ensure the best possible outreach, the course will be delivered through e-learning. Through a multiple-instructional setting, the goal is to achieve the learning objectives by means of learning technologies that match personal learning styles and by the inclusion of non-linear learning that aims at the development of just-in-time skills of adult learners. At the same time, in order to allow participants maximum flexibility of scheduling , the learning will be conducted in an asynchronous manner. Using a state-of-the-art training architecture, UNITAR will combine self-learning with assessments and online discussions. The pedagogy - adapted specifically to professionals in full-time work - will help train participants through various experiences: absorb (read); do (activity); interact (socialize); reflect (relate to one’s own reality).
This course is oriented toward economists and financial specialists that would be dealing with financial risk management issues. However, this course may be interesting for all professionals dealing with treasury operations and to persons belonging to the academia who would be interested in financial risk management issues, because it provides a basic summary of the present tools in use in order to cope with financial risks.
A certificate of completion will be issued by UNITAR to all participants who complete the course-related assignments and assessments successfully. Course schedule is subject to change. Course fee is non-refundable but transferrable to another course or participant and subject to change as per UNITAR's policy on pricing.
Recommended hardware and software requirements for taking our e-learning courses:
- Platform: Windows XP sp3, Vista sp2, Windows 7 sp1, MacOS X.
- Hardware: 2 GB of RAM and higher for Vista and Windows 7.
- Software: Microsoft Word, Microsoft Excel, Microsoft Powerpoint and Adobe Acrobat Reader (downloadable for free at adobe.com).
- Browser: Internet Explorer 8 or higher; Mozilla Firefox 8 or higher.
- Internet connection: 128kbps and higher.