Spring 2027

Financial Statistics

Financial Statistics covers advanced statistical methodologies for modern finance, including binomial trees, GARCH, Black-Scholes, and stochastic volatility models, with emphasis on data analysis and risk management.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 7

Past student reviews · Yazhen Wang

Assignments were considered difficult, with homeworks described as challenging and exam questions characterized as extremely tricky. Students reported that complex R coding tasks exceeded the stated basic requirements.

What you’ll be able to do

  • Describe basic stochastic models used in pricing financial instruments.

  • Develop statistical inferences for financial applications to volatility analysis and risk management.

  • Use computer packages to perform statistical analysis of financial data.

  • Interpret statistical analysis in the context of financial applications

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(STAT 333, 340, or ECON 410) and (MATH/​STAT 309,STAT 311,MATH 331,MATH/​STAT 431, or MATH 531), graduate/professional standing, or declared in Statistics VISP

STAT 461

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

YAZHEN WANG is recorded teaching in Fall 2019, Fall 2020. 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 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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Student experience

the class

Past student reviews · Yazhen Wang

Students described the course as highly disorganized, noting that lectures were difficult to follow and lacked a standard textbook or adequate study materials for exams.

Recent recorded grades — Fall 2023: 3.57 GPA, 78.9% A/AB (n=38 letter grades); Fall 2024: 3.73 GPA, 79.2% A/AB (n=24 letter grades); Fall 2025: 3.63 GPA, 74.5% A/AB (n=47 letter grades).

difficulty & workload

Past student reviews · Yazhen Wang

Assignments were considered difficult, with homeworks described as challenging and exam questions characterized as extremely tricky. Students reported that complex R coding tasks exceeded the stated basic requirements.

Past student reviews · Yazhen Wang

Learners found it impossible to find outside help because the instructor did not follow a textbook, and they criticized the lack of provided study resources for testing.

Topics

  • Discrete models like binomial trees and GARCH
  • Continuous models like the Black-Scholes model
  • Stochastic volatility models
  • Risk management applications

Skills

  • Describing stochastic models for pricing financial instruments
  • Developing statistical inferences for volatility analysis and risk management
  • Using computer packages for statistical analysis of financial data
  • Interpreting statistical analysis in financial contexts

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1316 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

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.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
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Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "eeba5fe3ef5083e7484b4a4e8d3cb548e593d31946ff51010e57be869b514641",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}