Chapter 12: Time-Series Analysis

In this section

• Autoregressive and Moving Average Models
  Deep dive into AR and MA models, exploring their mathematical foundations, practical applications in finance, and the essentials for CFA candidates seeking to master time-series forecasting.
• ARMA and ARIMA Processes
  Comprehensive exploration of ARMA and ARIMA models, their assumptions, stability conditions, and practical forecasting techniques within time-series analysis.
• Unit Roots, Stationarity, and Forecasting
  Explore the critical role of stationarity in time-series modeling, learn about unit roots and how to detect them, and discover practical forecasting insights for financial data.
• Seasonality and Trend Analysis
  Explore methods to detect and model time-series patterns that repeat at fixed intervals and exhibit long-term upward or downward movement, including SARIMA techniques and best practices.
• Autocorrelation and Partial Autocorrelation Functions
  Discover how ACF and PACF help identify time-series structures by measuring correlations across different lags, an essential step in building ARMA and ARIMA models for financial forecasting.
• Cointegration and Error Correction Models
  Explore the theory and practical applications of cointegration and error correction models in time-series analysis, focusing on financial markets, testing procedures, and modeling strategies.