Financial Forecasting and Simulation

Course Description

Forecasts of financial variables play a prominent role in financial and business decision-making. This course provides an overview of modern statistical and econometric methods for predicting financial variables and evaluating forecasts. Students will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. We emphasize intuitive understanding of the basic concepts and techniques and practical applications to real-world data. Topics covered include linear projections; modeling and forecasting trend, seasonality, and cycles; AR, MA, ARMA, ARIMA, and VAR models; forecasting with fundamentals; conditional forecasting models and scenarios analysis (stress testing); evaluating and combining forecasts; unit roots, cointegration, and stochastic trends; smoothing and shrinkage; ARCH, GARCH, and volatility forecast; unobserved components models and Kalman filter forecasting; data snooping, bootstrap, and reality check. Prerequisites: Ability to write nontrivial code in Matlab and Python. Basic calculus, linear algebra, probability and statistics, and econometrics. The course is available only to students enrolled in the master's of quantitative finance program unless given special permission by the director of the program.

Code

22:839:637

Credits

3

Subject

839