Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».