Testing and finding the generating functions of an option pricing mechanism through market data

We study dynamic pricing mechanisms of financial derivatives. A typical model of such pricing mechanism is the so-called g-expectation defined by solutions of a backward stochastic differential equation with g as its generating function. Black-Scholes pricing model is a special linear case of this pricing mechanism. We are mainly concerned with two types of pricing mechanisms in an option market: the market pricing mechanism through which the market prices of options are produced, and the ask-bid pricing mechanism operated through the system of market makers. The later one is a typical nonlinear pricing mechanism. Data of prices produced by these two pricing mechanisms are usually quoted in an option market. We introduce a criteria to test if a dynamic pricing mechanism under investigation is a g-pricing mechanism. This domination condition was statistically tested using CME data documents. The result of test is significantly positive. We also provide some useful characterizations of a pricing mechanism by its generating function

Bounding the efficiency of road pricing

This paper deals with the following question associated with congestion pricing in a general network with either fixed or elastic travel demand: what is the maximum efficiency loss of a general second-best pricing scheme due to inexact marginal-cost pricing in comparison with the first-best pricing or system optimum case? A formal answer to this question is provided by establishing an inefficiency bound associated with a given road pricing scheme. An application of the methods is provided for the practical trial-and-error implementation of marginal-cost pricing with unknown demand functions

A resource-advantage perspective on pricing: shifting the focus from ends to means-end in pricing research?

This paper contributes to a long-lasting debate between practitioners who argue that academia is unable to understand what pricing is all about and academics who criticize practitioner pricing approaches for lacking rigor or rationality. The paper conceptualizes a resource-advantage (R-A) perspective on pricing by drawing on the R-A theory of competition. After a review of R-A theory, the paper integrates the price discretion concept and pricing as a spanning competence by introducing a separation between resources that create and resources that extract value, thereby expanding R-A theory to pricing. The perspective aims to shed light on how the process of competition helps organizations to learn/benefit from pricing capabilities. The research shifts the focus of pricing research from an equilibrium-based static view to a dynamic, disequilibrium-provoking pricing competence. In this way, it draws attention to what is perhaps most relevant to pricing in practice: the actual means necessary to determine price

The Design of Arbitrage-Free Data Pricing Schemes

Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database. Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction. In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage. We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output. Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice. We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions. Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.Comment: full pape