Commentary on "On asset-liability matching and federal deposit and pension insurance"
Bibliographic record
Abstract
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?
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.095 | 0.066 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".