ADVANCED FINANCIAL STATISTICS

STAT 461

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.

STAT 849

Gain thorough grounding in modern statistical methods and theory. Classical and modern statistical techniques, including linear regression models, diagnostic tools, prediction, generalized linear regression models, random effect models, experimental design, and model selection. Apply these techniques to dataset in specific contexts.

STAT 613

Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design.

ECON 709

Probability distributions, statistical inference; multiple linear regression; introduction to econometric methods. It is expected that students will have completed three semesters calculus (such asMATH 234) and linear algebra (such asMATH 340).

STAT 349

Autocorrelation; stationarity and non-stationarity; heteroscedasticity; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications for financial time series, social sciences and environmental studies.