Understanding and Applying Financial Risk Modeling Techniques
Pluralsight
Course Summary
Financial risk modeling is at the intersection of two hot trends: Fintech and Big Data. This course covers three financial risk modeling techniques: covariance matrices, factor models, and value-at-risk.
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Course Description
Financial risk modeling is back in the limelight these days because of its place at the intersection of two hot trends: Fintech and Big Data. Enthusiasm about the intersection of technology and finance is tempered by caution born from past financial risk management failures, such as those witnessed during the Subprime Crisis. In this course, Understanding and Applying Financial Risk Modeling Techniques, you'll learn the details of three related financial risk modeling techniques: covariance matrices, factor models, and value-at-risk. First, you'll discover risk, uncertainty, and standard deviation. Next, you'll explore the role of covariance matrices in modeling risk. Then, you'll go through building scenario-based stress tests using factor models. Finally, you'll learn how to implement a robust risk modeling approach using Excel, VBA, R, and Python. By the end of this course, you'll have a good understanding of how financial risks of all types can be quantified and modeled.
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Course Syllabus
Course Overview- 1m 43s
—Course Overview 1m 43sUnderstanding Financial Risk- 35m 34s
—Risk and Uncertainty 7m 17s
—Idiosyncratic and Systemic Risk 7m 3s
—Case Studies in Risk Management 7m 52s
—Mean and Variance 6m 39s
—Covariance Matrices 3m 36s
—Coming up Next 3m 5sMeasuring Financial Risk Using Models- 38m 0sImplementing Financial Risk Models in Excel and VBA- 38m 51sImplementing Financial Risk Models in R- 28m 4sImplementing Financial Risk Models in Python- 29m 52s