Advanced Statistical Methods for Finance
( 25 Modules )

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

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Advanced Statistical Methods for Finance ( 25 Modules )

Advanced Statistical Methods for Finance
( 25 Modules )