Why the Diploma in Financial and Energy Markets Data Modelling?
The specialization in FEMDM will give you the skills and expertise that you need to flourish in a global economy that is increasingly data-driven.
Graduates will be able to apply these concepts and tools in the financial and energy industries, analyzing sets of voluminous and complex data to effectively plan future trading and transactions and manage their associated risk.
Possible Career Paths
Learners who opt for the FEMDM specialization will be able to find employment opportunities in Alberta’s finance sector as data scientist or data analyst. Identified as a key economic sector in the province, Alberta’s financial sector includes financial institutions with capabilities for research, sales, trading, investment banking and corporate banking.
This specialization aims to produce graduates capable of managing and analyzing big financial data of energy markets and the associated risks associated with those overwhelming data by providing them with the highly technical mathematical, statistical, and data analytics skills required to work in the intersection of financial and energy markets.
Once students have completed the certificate's four courses (DATA 601, 602, 603 and 604) they are able to enter the diploma program, with a specialization in Financial and Energy Markets Data Modelling, and complete the following courses:
Design of surveys and data collection, bias and efficiency of surveys. Sampling weights and variance estimation. Multi-way contingency tables and introduction to generalized linear models with emphasis on applications.
Advancement of the linear statistical model including introduction to data transformation methods, classification, model assessment and selection. Exposure to both supervised learning and unsupervised learning.
Provides more advanced coverage of tools and techniques for mathematical modelling, machine learning and statistical prediction based on big data in financial and energy markets, their management and processing, and building applications that leverage large datasets. It will include: Overview of financial and energy market data. A simple financial and energy market models. Risk-free and risky assets data modelling. Discrete-time financial and energy market data modelling. Continuous-time financial and energy market data modelling. Modelling of forwards, futures, swaps in financial and energy markets. Risk-neutral valuation with financial and energy markets data. Options and option pricing with financial and energy markets data. Macro perspective: World energy data trends-crude oil, natural gas, and electricity. Renewable energy data: wind, solar and others. Many examples and cases are discussed to gain hands on experience.
An examination of tools and methods, used in financial and energy markets data analysis, applying to risk management designed for this course. As technology advances, methods to explore financial and energy markets data have expanded to include basic and advanced analytic and statistical tools, as well as machine learning techniques and other advanced methods. The course will be based around usage of one or more finance and energy markets data analysis packages/programs to analyze different types of risks to be managed based on financial and energy markets data.
Modelling of dynamics for forwards and futures in with financial and energy markets data. Black-76 and Margrabe formulas for pricing of futures and options contracts. Gaussian exponential factor models. Modelling paradigms beyond Black-76. Data modelling with stochastic interest rates. Schwartz’s one-, two- (with stochastic convenience yield) and three-factor (with stochastic convenience yield and interest rate) models for pricing forward and futures data in energy markets. Modelling of high-frequency and algorithmic trading data.