An experimental approach to valuing new differentiated products

An experimental store was created to evaluate initial demand for locally produced and guaranteed tender steak products as a more realistic alternative to contingent valuation (CV) and dichotomous-choice experimental methods. Strengths of the approach are incentive compatibility, a realistic consumption set, and a familiar choice environment. Consumers selected among USDA Choice, premium quality, lean, guaranteed tender and locaily produced strip steaks. A double-hurdle count data model indicated initial willingness-to-pay (WTP) for locally produced steak comparable to prior CV results, but demand was highly elastic. Demand for premium quality steak crowded out demand for the guaranteed tender product, contrasting with prior dichotomous-choice experimental results.     

Modeling total cholesterol as predictor of mortality: the low-cholesterol paradox

Elevated total cholesterol is well-established as a risk factor for coronary artery disease and cardiovascular mortality. However, less attention is paid to the association between low cholesterol levels and mortality--the low cholesterol paradox. In this paper, restricted cubic splines (RCS) and complex survey methodology are used to show the low-cholesterol paradox is present in the laboratory, examination, and mortality follow-up data from the Third National Health and Nutrition Examination Survey (NHANES III). A series of Cox proportional hazard models, demonstrate that RCS are necessary to incorporate desired covariates while avoiding the use of categorical variables. Valid concerns regarding the accuracy of such predictive models are discussed. The one certain conclusion is that low cholesterol levels are markers for excess mortality, just as are high levels. Restricted cubic splines provide the necessary flexibility to demonstrate the U-shaped relationship between cholesterol and mortality without resorting to binning results. Cox PH models perform well at identifying associations between risk factors and outcomes of interest such as mortality. However, the predictions from such a model may not be as accurate as common statistics suggest and predictive models should be used with caution.     

Empirical likelihood block bootstrapping

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.     

Convertible Bonds and Stock Liquidity

This paper shows that the probability of exercise of convertible bonds issued against a firm’s stock directly affects the liquidity of the stock itself. Using the ratio of absolute stock return to its dollar volume as a proxy for stock liquidity I demonstrate that there is a direct and positive relationship between conversion probability and stock liquidity while controlling for firm size, book to market equity value and firm beta. I describe the effect of unlisted convertible debt on the liquidity of listed firms in the US, Korea and Singapore. The effects of conversion probability on stock liquidity are less pronounced for smaller firms, which helps explain time series variations in the liquidity premiums for smaller firms over time. The relationship between convertibles and stock liquidity is mainly attributed to the expected increase in the number of shares available for trade upon conversion and the expected change in the capital structure of the firm.     

Improving Skewness of Mean-Variance Portfolios

Abstract

The widely accepted belief that asset returns and insurance product line margins are not normally distributed has motivated the use of skewness (or higher than second-order moments) in the context of optimal risk-reward portfolio allocation. Here we propose an optimization-based methodology to substantially improve the skewness of portfolios in the mean-variance efficient frontier. Unlike other related methods, the proposed methodology is very intuitive, noniterative, and simple to implement, and it can be readily and efficiently carried out using state-of-the-art optimization solvers. These characteristics should be very appealing to risk managers.     

Securitization and regulatory arbitrage within the ABCT framework

This paper discusses the consequences of securitization and how it links to the Austrian Business Cycle Theory (ABCT). The argument that securitization is behind fiduciary credit expansion preceding the 2008 crisis is incomplete. Consolidated balance sheet analysis demonstrates that securitization per se actually sterilizes the inflationary effect of previous fiduciary credits by transforming them into credits backed by voluntary savings. This sterilization stage is subsequently followed by new fiduciary credits issuance as securitization creates excess reserves and excess capital for banks. However, when securitization is used as a tool to implement arbitrage strategies of the Basel prudential rules, it enables banks to create more fiduciary credit while time preference remains unchanged. This creates the conditions for business cycle amplification.     

How does financial performance affect marketing? Studying the marketing-finance relationship from a dynamic perspective

In this article, the authors provide a dynamic framework for understanding the relationship between marketing and financial performance. They suggest that firms market to financial markets as well as to consumption markets and that some mixture provides superior long-term performance. The authors discuss the potential pitfalls of overemphasizing one market to the detriment of another and then provide a theoretical model of the factors that influence the extent of marketing to financial markets. This is followed by a discussion of implications for theory, practice, and future research. Mitchell J. Lovett (mitchell.lovett@duke.edu) is a Ph.D. student in the Fuqua School of Business at Duke University. His current research interests include consumer and seller learning in dynamic environments, competitive and dynamic marketing strategies, and the role of marketing in innovation. Jason B. MacDonald (jmacdona@boisestate.edu) is an associate professor of marketing in the College of Business and Economics at Boise State University. His current research interests include the role of marketing in investor relations and Internet marketing strategy.     

Causality: a Statistical View

Statistical aspects of causality are reviewed in simple form and the impact of recent work discussed. Three distinct notions of causality are set out and implications for densities and for linear dependencies explained. The importance of appreciating the possibility of effect modifiers is stressed, be they intermediate variables, background variables or unobserved confounders. In many contexts the issue of unobserved confounders is salient. The difficulties of interpretation when there are joint effects are discussed and possible modifications of analysis explained. The dangers of uncritical conditioning and marginalization over intermediate response variables are set out and some of the problems of generalizing conclusions to populations and individuals explained. In general terms the importance of search for possibly causal variables is stressed but the need for caution is emphasized.     

MillicentCox, LLC

Millicent Cox is the Principal Economist of MillicentCox, LLC in San Diego. Prior experience includes positions with the University of California at San Diego, the Rand Corporation, and the University of Southern California. She holds a Ph.D. from the University of Southern California, an M.A. from the Claremont Graduate School, and a B.A. from New York University. JEL Classification M210, R230