Financial Risk Management with R

Duke uses financial-risk questions to develop an applied R workflow. Data retrieval and return calculations lead into models with normal distributions, non-normal behavior and volatility clustering. The sequence is useful for learners who want to connect code with assumptions and understand why the same dataset can produce different risk estimates under different modeling choices.

What you will learn

• Retrieve data and calculate financial returns in R.
• Compare distributional assumptions in risk analysis.
• Explain how changing volatility affects a model’s interpretation.

Who this suits

The course begins with an R introduction, but probability and basic financial-data familiarity will help you assess the examples critically. Review the coding environment and expected mathematics before enrolling.

Before you enroll

An instructional risk model does not predict an investment outcome. Check data quality, time periods and model assumptions, and distinguish an educational calculation from a decision about a real portfolio.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for included assessments, certificate terms, trials and any financial-aid options. Access conditions can change; review the current offer before paying.

Put it into practice

Use permitted sample data to compare two risk-model assumptions. Record the input period and calculation choices, then explain why the estimates differ and what each model leaves unresolved.

Source and review

This overview is based on the provider's published information, checked on 14 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.