Consumption Growth as a Risk Factor? Evidence from Canadian Financial Markets. This version: February 2002
Bibliographic record
Abstract
A linear factor model is used to link the returns on a number of assets from the Toronto Stock Exchange and Canadian bond market to observable risk factors. The proxies used for the underlying state variables are those generally identified by the Capital Asset Pricing Model (CAPM) and the Intertemporal or Consumption-based Capital Asset Pricing Model (CCAPM), namely the return on wealth, as measured by the return on the market portfolio, and consumption growth. The econometric model exploits the restrictions of the linear factor model to estimate conditional betas and to evaluate the size of the risk premia. Results show that the market risk premium is very precisely estimated and does not seem to vary much over the period considered. The estimated mean market risk premium is positive and it contributes to explain an important part of the excess returns on the assets considered in this analysis. On the other hand, the estimated consumption risk premium is much more variable and its impact on excess returns has decreased substantially during the last decade compared to the previous thirty years or so. JEL Classification: G10, G12 Key words: Asset pricing, multifactor model, risk premia, conditional betas. We would like to thank Steve Ambler, Russell Davidson, Alain Guay, Michel Normandin as well as seminar
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".