Additional cautionary notes about the Pearson's correlation coefficient

In this paper we draw attention to two usually-neglected problems with the use of the Pearson's correlation coefficient for ordinal scores. We refer to the questions of substantial differences between the marginal distributions of the two variables to be correlated, and the number of ties on each one of the distributions or on both. Finally, we propose a correction factor for the Pearson's correlation coefficient when the number of ties is large. Some empirical examples are given in order to demonstrate these points.     

Liquidation triggers and the valuation of equity and debt

Many bankruptcy codes implicitly or explicitly contain net-worth covenants, which provide the firm’s bondholders with the right to force reorganization or liquidation if the value of the firm falls below a certain threshold. In practice, however, default does not necessarily lead to immediate change of control or to liquidation of the firm’s assets by its debtholders. To consider the impact of this on the valuation of corporate securities, we develop a model in which liquidation is driven by a state variable that accumulates with time and severity of distress. We model a dynamic grace period for the liquidation event. Recent or severe distress events may have greater impact on the liquidation trigger. Our model can be applied to a wide array of bankruptcy codes and jurisdictions.     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.     

Visitation at capacity-constrained tourism destinations: Exploring revenue management at a national park

This study explores issues related to the introduction of revenue management principles in the mechanism of allocating permits to visitors. Specifically, the study looks at ways in which backcountry hikers in Grand Canyon National Park, a World Heritage Site listed among America’s most visited tourism attractions, value a particular allocation mechanism for a permit application. From a stratified random sampling scheme with a 76% response rate, over 1400 overnight backcountry hikers reported the potential for considerable increased revenues. At the same time the findings indicate that certain demographics and user groups will not participate in the modifications, and thus, may be less likely to get a permit when they apply for one. Any policy implementation from such an analysis needs to consider the implications of privileging those who are both willing and able to pay more for access, and effectively excluding others from the same opportunity.     

Efficient Calibration of Trinomial Trees for One-Factor Short Rate Models

In this paper we propose a computationally efficient implementation of general one factor short rate models with a trinomial tree. We improve the Hull–Whites procedure to calibrate the tree to bond prices by circumventing the forward rate induction and numerical root search algorithms. Our calibration procedure is based on forward measure changes and is as general as the Hull–White procedure, but it offers a more efficient and flexible method of constructing a trinomial term structure model. It can be easily implemented and calibrated to both prices and volatilities. JEL classification G13, C6