To make high-quality research more accessible and easier to explore.

Fields:
3 results ✕ Clear filters

Decomposing swap spreads

Journal of Financial Economics 2008 88(2), 375-405
We analyze a six-factor model for Treasury bonds, corporate bonds, and swap rates and decompose swap spreads into three components: a convenience yield from holding Treasuries, a credit risk element from the underlying LIBOR rate, and a factor specific to the swap market. The convenience yield is by far the largest component of spreads. There is a discernible contribution from credit risk as well as from a swap-specific factor with higher variability which in certain periods is related to hedging activity in the mortgage-backed security market. The model also sheds light on the relation between AA hazard rates and the spread between LIBOR rates and General Collateral repo rates and on the level of the riskless rate compared to swap and Treasury rates.

Corporate bond liquidity before and after the onset of the subprime crisis

Journal of Financial Economics 2012 103(3), 471-492 open access
We analyze liquidity components of corporate bond spreads during 2005–2009 using a new robust illiquidity measure. The spread contribution from illiquidity increases dramatically with the onset of the subprime crisis. The increase is slow and persistent for investment grade bonds while the effect is stronger but more short-lived for speculative grade bonds. Bonds become less liquid when financial distress hits a lead underwriter and the liquidity of bonds issued by financial firms dries up under crises. During the subprime crisis, flight-to-quality is confined to AAA-rated bonds.

Generalized recovery

Journal of Financial Economics 2019 133(1), 154-174 open access
We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. We make no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015). Recovery is feasible when the number of maturities with observable prices is higher than the number of states of the economy (or the number of parameters characterizing the pricing kernel). When recovery is feasible, our model allows a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return and other recovered statistics.