Commentary on "On asset-liability matching and federal deposit and pension insurance"
Notice bibliographique
Résumé
The PBGC assumes responsibility for a plan’s defined-benefit pension obligations when two conditions are simultaneously met: the sponsoring firm is financially distressed and the pension plan is sufficiently underfunded. As such, PBGC insurance is a compound put option held by definedbenefit plan sponsors, and PBGC liabilities can be valued using options pricing methods. Recently, Wendy Kiska and Marvin Phaup of the Congressional Budget Office (CBO) and I have developed an options-pricing model to quantify the PBGC’s prospective net costs and to serve as a tool to evaluate the effect of various policy alternatives. The results described here are drawn from that CBO (2005) analysis. To briefly describe the model, it employs a Monte Carlo simulation that takes into account the evolution of firm assets, firm liabilities, pension assets, and pension liabilities and their interaction with program rules. For simplicity, firm and pension assets are assumed to be stochastic, whereas firm and pension liabilities are taken to be deterministic. Both firm and pension assets are affected by correlated market risk, and taking into account this risk adds significantly to the estimated value of the put option. The model is calibrated using 2004 data covering the top 1,179 companies with defined-benefit pension plans. Although reported underfunding in 2004 totaled $450 billion, the forward-looking estimate of the PBGC’s net cost is only a fraction of this. Over a 10-year horizon, we project a net cost of T he recent failures of several very large corporations with severely underfunded pension plans (e.g., United Airlines, U.S. Airways, and Bethlehem Steel) have made the risk exposure of the Pension Benefit Guaranty Corporation (PBGC), the government agency that insures defined-benefit plans, front page news. Further, the prospect that other large corporations are likely to follow has motivated legislators to introduce several new proposals aimed at limiting the PBGC’s risk exposure. In his paper, Bodie (2006) reminds us of the straightforward but often ignored fact that much of the risk to the PBGC could be avoided if limits were imposed on the share of pension assets invested in stocks and other risky assets. Presumably, the lack of interest by Congress in imposing such restrictions is due to very strong resistance from the business community. The fundamental question, then, is why do managers believe that it is imperative to invest pension assets predominantly in stocks, despite the volatility in funding requirements that they have experienced following this strategy? In these comments I will focus on two broad questions raised by Bodie’s analysis: First, what are the main drivers of the PBGC’s risk exposure? Second, why do pension managers choose to invest pension assets the way they do and when should the optimal hedge portfolio contain some stocks?
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 | 0,011 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,010 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,010 | 0,004 |
| Intégrité de la recherche | 0,095 | 0,066 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,005 |
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 ».