The world of finance has driven the development of many sophisticated techniques for data analysis. In this episode Paul Stafford shares his experiences working in the realm of risk management for financial exchanges. He discusses the types of risk that are involved, the statistical methods that he has found most useful for identifying strategies to mitigate that risk, and the software libraries that have helped him most in his work.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Your host as usual is Tobias Macey and today I’m interviewing Paul Stafford about building risk models to guard against financial exchange rate volatility
- How did you get introduced to Python?
- What are the principles involved in risk management, and how are statistical methods used?
- How did you get involved in financial markets?
- In what ways did your background in science and engineering prepare you for work in finance and risk management?
- What are the tools that you have found most useful in your career in finance?
- How have recent trends such as the widespread adoption of deep learning impacted the capabilities and risks present in foreign exchange strategies?
- What are the challenges that you face in obtaining and validating the input data that you are relying on for building financial and statistical models?
- How has the volatility of the pandemic impacted the robustness and resilience of your predictive capabilities?
- What are the areas where the available tools are typically insufficient?
- What are the most interesting, innovative, or unexpected strategies or techniques that you have seen applied to risk management?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working in risk management?
- What are the economic and industry trends that you are keeping a close eye on for your work at Deaglo and your own personal projects?
Keep In Touch
- The Vault (movie)
- Motorcycle Trip of the Grand Canyon
- Deaglo Partners, LLC.
- Value At Risk (VaR)
- Black-Scholes Equation
- Linear Algebra
- Principal Component Analysis
- Eigenvectors and Eigenvalues
- Markov Chain Monte Carlo
- Violin Plot
- Bayesian Regression
- Constrained Optimization
- Smart Contracts
- Behavioral Finance
- Black Swan by Nassim Nicholas Taleb (affiliate link)
- SciPy Convention
- Sentiment Analysis