Times-series Analysis (2025 Level II CFA® Exam –Quantitative Methods–Module 5)

In this comprehensive lesson, Professor James Forjan, PhD, CFA, teaches Time-Series Analysis for the CFA Level II Quantitative Methods topic. This reading builds directly on regression analysis concepts from Level I and introduces time-based data modeling for financial forecasting and trend analysis.

You’ll learn how to identify patterns in financial data, test for stationarity, model relationships between past and current values, and forecast future outcomes using autoregressive and moving-average processes.

What You’ll Learn:
Linear and log-linear trend models
Covariance stationarity and non-stationary time series
Autoregressive (AR) models and forecasting methods
Serial correlation and the Durbin-Watson test
Mean reversion and random walks
Unit roots and the Dickey-Fuller test
Seasonality, co-integration, and ARCH models
Choosing and validating time-series models

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Topic 1 – Quantitative Methods
Module 4 – Times-series Analysis
0:00 Introduction and Learning Outcome Statements
1:24 LOS: Calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients
5:45 LOS: Describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models
7:24 LOS: Explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary
8:45 LOS: Describe the structure of an autoregressive (AR) model of order p and calculate one- and two period-ahead forecasts given the estimated coefficients
14:07 LOS: Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series
18:58 LOS: Explain mean reversion and calculate a mean-reverting level
21:06 LOS: Contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion
25:01 LOS: Explain the instability of coefficients of time-series models
27:30 LOS: Describe characteristics of random walk processes and contrast them to covariance stationary processes.
31:24 LOS: Describe implications of unit roots for time-series analysis, explain when unit-roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model
33:25 LOS: Describe the steps of the unit root test for non-stationary and explain the relation of the test to autoregressive time-series models
36:49 LOS: Explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag
42:35 LOS: Explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series
46:59 LOS: Explain how time-series variables should be analyzed for nonstationary and/or cointegration before use in linear regression
53:27 LOS: Determine an appropriate time-series model to analyze a given investment problem and justify that choice

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