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Compared to offline matching markets, online matching platforms improve search in the matching process but at the same time increase the problem of authenticating the features and credentials of prospective matches. This paper examines the interplay between these two processes in online matching, using game-theoretic models. We examine whether an online matching platform should target a broad market of match-seekers or an exclusive group of high-value match-seekers, and how the platform should price its search and authentication services. Our results provide valuable insights for online matching platforms regarding the decision to offer authentication services in addition to search services, and guidelines for the pricing and positioning of these services. For instance, we show that the complementarity of the platform's optimal pricing for search and authentication services can justify the platform's offering an authentication service as a loss leader, and that higher-quality authentication services may not always justify higher authentication fees. We also develop guidelines for the platform's optimal strategies for different market conditions.

Compared to offline matching markets, online matching platforms improve search in the matching process but at the same time increase the problem of authenticating the features and credentials of prospective matches. This paper examines the interplay between these two processes in online matching, using game-theoretic models. We examine whether an online matching platform should target a broad market of match-seekers or an exclusive group of high-value match-seekers, and how the platform should price its search and authentication services. Our results provide valuable insights for online matching platforms regarding the decision to offer authentication services in addition to search services, and guidelines for the pricing and positioning of these services. For instance, we show that the complementarity of the platform's optimal pricing for search and authentication services can justify the platform's offering an authentication service as a loss leader, and that higher-quality authentication services may not always justify higher authentication fees. We also develop guidelines for the platform's optimal strategies for different market conditions.

Firms increasingly deploy algorithmic pricing approaches to determine what to charge for their goods and services. Algorithmic pricing can discriminate prices both dynamically over time and personally depending on individual consumer information. Although legal, the ethicality of such approaches needs to be examined as often they trigger moral concerns and sometimes outrage. In this research paper, we provide an overview and discussion of the ethical challenges germane to algorithmic pricing. As a basis for our discussion, we perform a systematic interpretative review of 315 related articles on dynamic and personalized pricing as well as pricing algorithms in general. We then use this review to define the term algorithmic pricing and map its key elements at the micro-, meso-, and macro levels from a business and marketing ethics perspective. Thus, we can identify morally ambivalent topics that call for deeper exploration by future research.

We formulate a model of utility for a continuous-time framework that captures aversion to ambiguity about both volatility and drift. Corresponding extensions of some basic results in asset pricing theory are presented. First, we derive arbitrage-free pricing rules based on hedging arguments. Because ambiguous volatility implies market incompleteness, hedging arguments determine prices only up to intervals. In order to obtain sharper predictions, we apply the model of utility to a representative agent endowment economy and study equilibrium asset returns. A version of the consumption capital asset pricing model is derived, and the effects of ambiguous volatility are described.

How do dealers price contemporary art in a world where objective criteria seem absent?Talking Pricesis the first book to examine this question from a sociological perspective. On the basis of a wide range of qualitative and quantitative data, including interviews with art dealers in New York and Amsterdam, Olav Velthuis shows how contemporary art galleries juggle the contradictory logics of art and economics. In doing so, they rely on a highly ritualized business repertoire. For instance, a sharp distinction between a gallery's museumlike front space and its businesslike back space safeguards the separation of art from commerce. Velthuis shows that prices, far from being abstract numbers, convey rich meanings to trading partners that extend well beyond the works of art. A high price may indicate not only the quality of a work but also the identity of collectors who bought it before the artist's reputation was established. Such meanings are far from unequivocal. For some, a high price may be a symbol of status; for others, it is a symbol of fraud. Whereas sociological thought has long viewed prices as reducing qualities to quantities, this pathbreaking and engagingly written book reveals the rich world behind these numerical values. Art dealers distinguish different types of prices and attach moral significance to them. Thus the price mechanism constitutes a symbolic system akin to language.

Data as a new production material has great potential value, and its value is reflected after the utility generated. So it is difficult to set prices for data products. This paper analyzes the characteristics of data products, builds a data product pricing model from a harmonious and mutual-beneficial perspective, and verifies the operability of the model in the simulation pricing strategy. The results of this research can help the construction and development of the data transaction market, and provide ideas for future research on data pricing.

We propose a spatial capital asset pricing model and a spatial arbitrage pricing theory (S-APT) that extend the classical asset pricing models by incorporating spatial interaction. We then apply the S-APT to study the comovements of eurozone stock indices (by extending the Fama–French factor model to regional stock indices) and the futures contracts on S&P/Case–Shiller Home Price Indices; in both cases, spatial interaction is significant and plays an important role in explaining cross-sectional correlation. The e-companion is available at https://doi.org/10.1287/mnsc.2016.2627 . This paper was accepted by Neng Wang, finance.

We show that the compensation for rare events accounts for a large fraction of the average equity and variance risk premia. Exploiting the special structure of the jump tails and the pricing thereof, we identify and estimate a new Investor Fears index. The index reveals large time-varying compensation for fears of disasters. Our empirical investigations involve new extreme value theory approximations and high-frequency intraday data for estimating the expected jump tails under the statistical probability measure, and short maturity out-of-the-money options and new model-free implied variation measures for estimating the corresponding risk-neutral expectations.

Over the years, many asset pricing studies have employed the sample cross-sectional regression (CSR) R² as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R² differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama-French three-factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.