Financial Microeconomics
Financial Microeconomics covers contemporary theory of financial markets, portfolio choice, and asset pricing.
Summary
Past student reviews · Marek Weretka
The course required completing at least eight problem sets involving Python and calculations, maintaining a relatively fast pace throughout the term.
What you’ll be able to do
Formulate the classic consumption-based framework and financial equilibrium
Derive the predictions for portfolio choice, asset pricing, and social efficiency
Recover Fisher prices for YTM of treasury bonds and use these prices to valuate fixed income assets (bonds, mortgages)
Recover Stochastic Discount Factor (SDF) from option prices and valuate risky assets (stocks, options)
Understand the implication of SDF for a risk premium
Model financial markets with default and collateral and systemic risk
Model thin markets with institutional investors and insider’s trading
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
MAREK WERETKA is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Fall 2025. 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 |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Past student reviews · Marek Weretka
Marek Weretka taught a fast-paced course that linked macro topics to financial micro, offering a manageable Python project alternative to the final exam.
Recent recorded grades — Fall 2023: 3.50 GPA, 68.0% A/AB (n=25 letter grades); Fall 2024: 3.70 GPA, 83.8% A/AB (n=74 letter grades); Fall 2025: 3.62 GPA, 76.5% A/AB (n=17 letter grades).
difficulty & workload
Past student reviews · Marek Weretka
The course required completing at least eight problem sets involving Python and calculations, maintaining a relatively fast pace throughout the term.
Past student reviews · Marek Weretka
One student noted that the option to substitute the final exam with a Python project was relatively easy to manage.
Topics
- Contemporary theory of financial markets, portfolio choice, and asset pricing.
- Consumption-based frameworks, financial equilibrium, and social efficiency.
- Fixed income valuation using Fisher prices and risky asset valuation using SDFs.
- Market models addressing default, collateral, systemic risk, and insider trading.
Skills
- Formulating consumption-based frameworks and deriving predictions for portfolio choice and asset pricing.
- Valuating fixed income and risky assets using Fisher prices and Stochastic Discount Factors.
- Modeling financial markets involving default, collateral, systemic risk, and insider trading.
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
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
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Selected offering source records
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Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
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
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"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
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