Recent recorded grades — Spring 2024: 3.71 GPA, 97.8% A/AB (n=45 letter grades); Spring 2025: 3.59 GPA, 82.1% A/AB (n=28 letter grades); Spring 2026: 3.82 GPA, 100.0% A/AB (n=19 letter grades).
Financial Econometrics
Financial Econometrics covers econometric analysis of financial time series, including linear and multivariate models, volatility estimation, and model testing.
Summary
Recent recorded grades — Spring 2024: 3.71 GPA, 97.8% A/AB (n=45 letter grades); Spring 2025: 3.59 GPA, 82.1% A/AB (n=28 letter grades); Spring 2026: 3.82 GPA, 100.0% A/AB (n=19 letter grades).
What you’ll be able to do
Demonstrate an understanding of the major theories of financial pricing
Use the theory of efficient markets to understand asset pricing
Use linear time-series econometric models for financial forecasting
Use volatility models to identify financial uncertainty
Formulate and estimate asset pricing models
Work with real financial data sets
Build models for portfolio optimization
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapProfessors
Spring 2027Historical instructors & teaching patterns
BRUCE HANSEN is recorded teaching in Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 15 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Recent recorded grades — Spring 2024: 3.71 GPA, 97.8% A/AB (n=45 letter grades); Spring 2025: 3.59 GPA, 82.1% A/AB (n=28 letter grades); Spring 2026: 3.82 GPA, 100.0% A/AB (n=19 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Linear econometric time series models
- Multivariate time series
- Volatility estimation
- Estimating and testing financial econometric models
- Major theories of financial pricing
- Theory of efficient markets and asset pricing
- Financial forecasting using linear time-series models
- Portfolio optimization models
Skills
- Understanding major theories of financial pricing
- Using efficient market theory for asset pricing
- Using linear time-series econometric models for financial forecasting
- Using volatility models to identify financial uncertainty
- Formulating and estimating asset pricing models
- Working with real financial data sets
- Building models for portfolio optimization
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Financial Econometrics
Recorded 2026-10-14Raw records
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"offering_id": "1274:296:025714",
"course_id": "ECON 724",
"course_uid": "course_1a281c40cbe54b8d697e1160",
"term_id": "1274",
"source_course_id": "025714",
"source_subject_id": "296",
"title": "Financial Econometrics",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
]Student reviews
Original comments behind the course and instructor summaries.
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "25ae5bfd92348ba02f5b8600c0ec4841da73214055b1d7ad5a1d30cc2ceb8887",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}