Bibliographic record
Abstract
Introduction This paper quantifies the gains associated with portfolio diversification in the market for bonds issued by the ten Canadian provinces. Diversification benefits from exposure to alternative markets have attracted great attention from individual and institutional investors. Several studies in this field have gauged the advantages of expanding the set of tradable assets from the domestic to the international equity market (among others, Bekaert and Urias, 1996; Errunza et al., 1999; and de Roon et al., 2001). Other works have investigated the gains from portfolio diversification across different classes or sets of assets (e.g., de Roon et al., 2003; Eun, et al., forthcoming). Our analysis contributes to the finance literature on diversification benefits by measuring the gains offered by the same class of assets, namely provincial bonds within the same country. Bonds issued by the Canadian provinces constitute an ideal dataset for an analysis of this type because they are traded in a homogeneous market environment using standardized contracts. To the authors' knowledge, this is the first paper to evaluate the value of portfolio diversification in the market for provincial bonds. Canadian provinces enjoy significant fiscal independence and can tailor bond issues to their needs. Expenditure and revenue levels vary widely across regional governments, so it might be hypothesized that the differences across provincial economies would be mirrored in their debt markets. If this were the case, provinces should offer differentiated financial products. Consequently, agents could enjoy significant improvements in their investment outlooks by forming portfolios with positions in bonds issued by several, if not by all, provincial governments. Our results contrast with this view. The following analysis documents the absence of diversification benefits across provincial bond markets for most of the issuing provinces. The evidence is particularly compelling when short-selling restrictions are taken into account. This study indicates that in a restricted trading environment, the advantages stemming from diversification across provinces might vanish for investors holding positions in a benchmark market consisting of one province only. Because anecdotal evidence suggests that shorting provincial bonds is not a feasible trading strategy, we have interpreted the results of our empirical analysis as indicating that individual provinces fail to yield diversification gains with respect to each other. The average correlation across assets is a rough measure of portfolio diversification benefits: the lower the correlation, the lower the risk associated with a diversified portfolio (e.g., Elton et al., Ch. 4, 2003). In our sample, the average correlation among bonds of different provinces is in the range of 0.9. A standard interpretation of this finding indicates that merging provincial bond markets hardly can be considered an effective strategy for obtaining a substantial reduction in portfolio variance. Much of the outcome of expanding the set of tradable assets is dependent upon how investors combine their augmented collection of investment possibilities. Therefore, a meaningful comparison of the investment opportunity set before and after the addition of some tradable securities requires a systematic approach that has as its foundation a common portfolio selection procedure. Mean-variance (MV) analysis (Markowitz, 1952) offers a suitable framework for such a comparison. In an MV framework, diversification gains can be gauged by the extent that the efficient frontier is shifted when the investment opportunity set is expanded. If the MV frontiers of both the benchmark and augmented asset collections are not significantly different, then the benchmark set of investments is said to span the additional investment opportunities. Huberman and Kandel (1987) proposed regression-based tests for spanning. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".