In a credit default swap (CDS) trade, a credit protection buyer acquires credit protection from the counterparty by paying a premium. The CDS premium has been viewed as a clean representation of the price of credit risk. As a result, in the past few years, researchers have used data on CDS trades where the reference entity is a corporation to determine the liquidity component of corporate bond spreads. Using a transaction dataset, the authors discover very large bid–ask spreads in CDS quotes. With a two-factor model, they show that such large bid–ask spreads can profoundly affect the estimation of credit risk, which in turn has a significant effect on the estimation of the liquidity spread for corporate bonds. Contrary to the literature, the authors show that although the bond and CDS markets appear to have two different values for the credit spread, once liquidity is accounted for they no longer find such a difference.

American options require numerical methods, namely lattice models, to provide accurate price estimates. The computations can become expensive when more than one state variable is involved. Analytical upper bounds can therefore provide a useful guideline for how high American values can reach. In this paper, we derive analytical (closed-form) upper bounds for American option prices under stochastic interest rates, stochastic volatility, and jumps where American option prices are difficult to compute with accuracy. In a stochastic volatility model (Heston (1993) and Scott (1997)) that has two random factors, we demonstrate that the upper bound only takes a very small fraction of the time that the American option needs to compute.

In this paper, we evaluate American-style, path-dependent derivatives with an artificial intelligence technique. Specifically, we use swarm intelligence to find the optimal exercise boundary for an American-style derivative. Swarm intelligence is particularly efficient (regarding computation and accuracy) in solving high-dimensional optimization problems and hence, is perfectly suitable for valuing complex American-style derivatives (e.g., multiple-asset, path-dependent) which require a high-dimensional optimal exercise boundary.

This paper tests whether the traditional futures hedge ratio (h_T) and the carry cost rate futures hedge ratio (h_c) vary in accordance with the Sercu and Wu (2000) and Leistikow et al. (2019) “h_c” theory. It does so, both within and across high and low spot asset carry cost rate (c) regimes. The high and low c regimes are specified by asset across time and across currency denominations. The findings are consistent with the theory. Within and across c regimes, h_T is inefficient and h_c is biased. Across c regimes, h_c’s Bias Adjustment Multiplier (BAM) does not vary significantly. Even though h_c’s bias-adjusted variant’s BAM is restricted to old data that is from a different c regime, the hedging performance of h_c and its bias-adjusted variant (=h_c × BAM), are superior to that for h_T. Variation in c may account for the h_T variation noted in the literature and variation in c should be incorporated into ex ante hedge ratios.