Module #1 Introduction to Advanced Statistical Methods in Finance Overview of the course, importance of statistical methods in finance, and review of basic statistical concepts
Module #2 Review of Linear Regression In-depth review of simple and multiple linear regression, assumptions, and diagnostics
Module #3 Time Series Analysis Introduction to time series analysis, autoregressive (AR) and moving average (MA) models, and stationarity
Module #4 ARIMA Models Autoregressive Integrated Moving Average (ARIMA) models, estimation, and forecasting
Module #5 Vector Autoregression (VAR) Models Introduction to VAR models, estimation, and applications in finance
Module #6 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Models Introduction to GARCH models, estimation, and applications in finance
Module #7 Extreme Value Theory Introduction to extreme value theory, modeling extreme events, and applications in finance
Module #8 Risk Analysis and Value-at-Risk (VaR) Introduction to risk analysis, VaR, and expected shortfall
Module #9 Introduction to Machine Learning in Finance Overview of machine learning, supervised and unsupervised learning, and model evaluation metrics
Module #10 Supervised Learning in Finance Application of supervised learning algorithms in finance, including logistic regression, decision trees, and random forests
Module #11 Unsupervised Learning in Finance Application of unsupervised learning algorithms in finance, including k-means clustering and principal component analysis
Module #12 Neural Networks in Finance Introduction to neural networks, deep learning, and applications in finance
Module #13 Panel Data Analysis Introduction to panel data analysis, fixed and random effects models, and applications in finance
Module #14 Survival Analysis in Finance Introduction to survival analysis, hazard functions, and applications in finance
Module #15 Instrumental Variables and Regression Discontinuity Introduction to instrumental variables, regression discontinuity, and applications in finance
Module #16 Financial Econometrics Introduction to financial econometrics, market microstructure, and high-frequency data
Module #17 Event Study Analysis Introduction to event study analysis, abnormal returns, and applications in finance
Module #18 Big Data Analytics in Finance Introduction to big data analytics, data mining, and applications in finance
Module #19 Python for Financial Data Analysis Introduction to Python programming, pandas, and NumPy for financial data analysis
Module #20 R for Financial Data Analysis Introduction to R programming, data manipulation, and visualization for financial data analysis
Module #21 Case Study:Equity Portfolio Optimization Application of advanced statistical methods to equity portfolio optimization
Module #22 Case Study:Credit Risk Modeling Application of advanced statistical methods to credit risk modeling
Module #23 Case Study:High-Frequency Trading Application of advanced statistical methods to high-frequency trading
Module #24 Case Study:Risk Management and Regulation Application of advanced statistical methods to risk management and regulation
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Statistical Methods for Finance career
