We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

FINANCIAL STATISTICS

STAT 461
Course Description

Stochastic models and statistical methodologies are widely employed in modern finance. The models and their inferences are very important for academic research and financial practices. Financial stochastic models and their statistical inferences with applications to volatility analysis and risk management, introduction to discrete models such as binomial trees and GARCH and stochastic volatility models as well as simple continuous models like the Black-Scholes model. The focus will be on statistical inference, data analysis and risk management regarding these models.

Prerequisties

(STAT 333 , STAT 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

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Fall

Grade Point Average
3.73

11.2% from Historical

Completion Rate
100%

2.07% from Historical

A Rate
70.83%

62.85% from Historical

Class Size
24

-31.71% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

STATISTICAL INFERENCE FOR DATA SCIENCE

STAT 612

Statistical models, methods and theory for parameter estimation, Bayesian approach to parameter estimation, methods and theory for hypothesis tests, confidence sets, two-sample testing and ANOVA, categorical data analysis, linear regression.

STATISTICAL MODELS FOR DATA SCIENCE

STAT 611

Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables.

MATHEMATICAL FOUNDATIONS OF BUSINESS ANALYTICS

GENBUS 317

Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications and will be used to analyze business data and make inferences and predictions.

FINANCIAL ECONOMETRICS

ECON 724

Introduction to econometric analysis of financial time series. Covers linear econometric time series models, multivariate time series, volatility estimation, and estimating and testing financial econometric models.

ADVANCED FINANCIAL STATISTICS

STAT 801

Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems.