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We develop a straightforward procedure to price derivatives by a bivariate tree when the underlying process is a jump-diffusion. Probabilities and jump sizes are derived are derived by matching higher order moments or cumulants. We give comparisons with other published results along with convergence proofs and estimates of the order of convergence. The bivariate tree approach is particularly useful for pricing long-term American options and long-term real options because of its robustness and flexibility. We illustrate the pedagogy in an application involving a long-term investment project.

We document trade price clustering in the futures markets. We find clustering at prices of x.00 and x.50 for S&P 500 futures contracts. While trade price clustering is evident throughout time to maturity of these contracts, there is a dramatic change when the S&P 500 futures contract is designated a front-month contract (decrease in clustering) and a back-month contract (increase in clustering). We find that trade price clustering is a positive function of volatility and a negative function of volume or open interest. In addition, we find a high degree of clustering in the daily opening and closing prices, but a lower degree of clustering in the settlement prices.

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