Chapter 6: Time‑Series Analysis

In this section

• Stationarity, Nonstationarity, and Unit Roots
  Explore the essentials of stationarity in time-series analysis, how to detect nonstationarity and unit roots, and practical methods like differencing to stabilize financial data for better forecasting.
• Trend Models: Linear vs. Log‑Linear
  Explore comprehensive insights into linear and log‑linear trend models, including when and how to use each model, how to interpret coefficients, and diagnosing model assumptions for time‑series data in financial analysis.
• Autoregressive (AR) Processes and Forecasting
  Dive into AR(p) models, stationarity conditions, and multi-step forecasting methods. Learn how to use model selection criteria, diagnose residuals, and avoid pitfalls for a robust time-series analysis strategy.
• Mean Reversion and Implications for Asset Pricing
  Learn how mean reversion works in time-series models, why it matters for asset pricing, and how to apply these insights in CFA Level II item set formats.
• Testing for Seasonality and Correcting It
  Explore key methods for detecting and addressing seasonality in time series, including ACF/PACF analysis, SARIMA models, and dummy-variable approaches, with practical financial examples and tips for the CFA® Level II exam.
• Autoregressive Conditional Heteroskedasticity (ARCH) Models
  A comprehensive exploration of ARCH and GARCH models in time-series analysis, focusing on volatility clustering, model formulations, parameter estimation, and practical applications in risk management.
• Vignette Examples: Building and Interpreting Time‑Series Models
  Explore integrated item-set style vignettes that demonstrate stationarity checks, AR models, GARCH for volatility, and seasonality—plus best practices for forecasting and interpreting findings under exam conditions.