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.
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.
An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and
studied at Stanford and INSEAD. He has worn many hats, each of which has involved
writing code and building models. He is passionately devoted to his hobby of laughing at
his own jokes.
Course Overview Hi everyone. My name is Vitthal Srinivasan. Welcome to my course Understanding and Applying Financial Risk Modeling Techniques. I am co-founder at a startup named Loonycorn. Prior to this I've worked at Google and studied at Stanford. If you treat the financial markets like a game where you go big or go home, chances are you will end up going home. We will learn from two famous episodes in the financial market in which many banks and hedge funds learned at their cost that it is a bad idea is to bet that tomorrow will look like yesterday. And what's worse is to do this with borrowed money. Some of the major topics that we will cover include risk, uncertainty and standard deviation; the role of covariance matrices and modeling risk; building scenario-based stress tests using factor models; quantifying worst case outcomes with value at risk; and lastly implementing a robust integrated risk modeling approach using Excel and VBA are an icon. By the end of this course you will have an excellent understanding of how financial risks of all types can be quantified and modeled and how those models can be implemented in three technologies. I hope you'll join me on this journey to learn how to embrace manageable risk and avoid unaffordable ones with the course Understanding and Applying Financial Risk Modeling Techniques on Pluralsight.