In the U.S., as in most countries with well-developed securities markets, derivative securities enjoy special protections under insolvency resolution laws. Most creditors are “stayed” from enforcing their rights while a firm is in bankruptcy. However, many derivatives contracts are exempt from these stays. Furthermore, derivatives enjoy netting and closeout, or termination, privileges which are not always available to most other creditors. The primary argument used to motivate passage of legislation granting these extraordinary protections is that derivatives markets are a major source of systemic risk in financial markets and that netting and closeout reduce this risk. To date, these assertions have not been subjected to rigorous economic scrutiny. This paper critically re-examines this hypothesis. These relationships are more complex than often perceived. We conclude that it is not clear whether netting, collateral, and/or closeout lead to reduced systemic risk, once the impact of these protections on the size and structure of the derivatives market has been taken into account.

This paper examines the absolute and relative robustness of two of the most common methods for estimating implied probability density functions (PDFs) – the double-lognormal approximating function and the smoothed implied volatility smile methods – using short sterling futures options and the FTSE 100 index options. The changes resulting from randomly perturbing quoted prices by no more than a half tick provide a lower bound on the confidence intervals of the summary statistics derived from the estimated PDFs. Our tests show that the smoothed implied volatility smile method dominates the double lognormal as a technique for estimating implied PDFs.

Current 1 -year forward rates on 1 - to 5-year U.S. Treasury bonds are information about the current term structure of 1-year expected returns on the bonds, and forward rates track variation through time in 1-year expected returns. More interesting, 1 -year forward rates forecast changes in the 1 -year interest rate 2- to l-years ahead, and forecast power increases with the forecast horizon. We attribute this forecast power to a mean-reverting tendency in the 1-year interest rate

ABSTRACT The purpose of this paper is to provide a test of a state‐dependent multinomial model of intertemporal changes in the term structure of interest rates. The theoretical background for the model comes from Ho and Lee (1986) . The current paper extends their model in several significant ways. First, we perform diagnostic tests on the data to demonstrate that the empirical results reject a binomial model in favor of a trinomial one. After theoretically deriving the appropriate trinomial model, the current paper extends their model to allow for state‐dependent shifts which are determined by the set of ex ante observable state variables. The methodology for the study utilizes OLS regressions to identify the exogenous explanatory variables which drive the hypothesized trinomial process of term structure evolution. The empirical tests indicate that the set of state variables explains a significant portion of the variability in the shifts of the term structure over time. The model also identifies and quantifies a set of variables which impact on changes in the term structure of interest rates.