Statistical Methods, Regression, and Tax-Aware Returns
Free CAIA Level II lesson in Methods and Models. 37 min read, ~5,549 words.
PCA outputs factor loadings and eigenvalues; each eigenvalue divided by the sum of eigenvalues gives that component's percent of variance explained. PCA differs from factor analysis in model assumptions, loading stability as factor count changes, and single-security identification; factor analysis requires factors driving at least two securities. Multicollinearity produces inaccurate...
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What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Exam Shortcuts
Learning objectives
- model types
- fi models intro
- bdt model
- credit risk economics
- structural model overview
- merton model
- kmv model
- reduced form models
- empirical credit models
- one period binomial
- multi period binomial
- tree prices formation
- convertible valuation
- callable bonds tree
- multifactor asset pricing
- fama french
- empirical mf challenges
- factor investing
- adaptive markets
- efficiently inefficient
- trend following
- divergence
- fundamental directional
- behavioral finance
- directional factors
- digital asset valuation
- pca statistical factors
- multifactor regression
- partial autocorrelations
- dynamic risk exposure
- changing correlation
- multifactor return approaches
- performance persistence
- rv overview
- statistical pairs equities
- pairs commodity spreads
- pairs rates fx
- rv market neutral risks
- depreciation tax shields
- tax deferral gains
- after tax comparisons
- transaction based indices
- appraisal based indices
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