1,605 research outputs found
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
A Theory of Pricing Private Data
Personal data has value to both its owner and to institutions who would like
to analyze it. Privacy mechanisms protect the owner's data while releasing to
analysts noisy versions of aggregate query results. But such strict protections
of individual's data have not yet found wide use in practice. Instead, Internet
companies, for example, commonly provide free services in return for valuable
sensitive information from users, which they exploit and sometimes sell to
third parties.
As the awareness of the value of the personal data increases, so has the
drive to compensate the end user for her private information. The idea of
monetizing private data can improve over the narrower view of hiding private
data, since it empowers individuals to control their data through financial
means.
In this paper we propose a theoretical framework for assigning prices to
noisy query answers, as a function of their accuracy, and for dividing the
price amongst data owners who deserve compensation for their loss of privacy.
Our framework adopts and extends key principles from both differential privacy
and query pricing in data markets. We identify essential properties of the
price function and micro-payments, and characterize valid solutions.Comment: 25 pages, 2 figures. Best Paper Award, to appear in the 16th
International Conference on Database Theory (ICDT), 201
Multi-keyword multi-click advertisement option contracts for sponsored search
In sponsored search, advertisement (abbreviated ad) slots are usually sold by
a search engine to an advertiser through an auction mechanism in which
advertisers bid on keywords. In theory, auction mechanisms have many desirable
economic properties. However, keyword auctions have a number of limitations
including: the uncertainty in payment prices for advertisers; the volatility in
the search engine's revenue; and the weak loyalty between advertiser and search
engine. In this paper we propose a special ad option that alleviates these
problems. In our proposal, an advertiser can purchase an option from a search
engine in advance by paying an upfront fee, known as the option price. He then
has the right, but no obligation, to purchase among the pre-specified set of
keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks
in a specified period of time. The proposed option is closely related to a
special exotic option in finance that contains multiple underlying assets
(multi-keyword) and is also multi-exercisable (multi-click). This novel
structure has many benefits: advertisers can have reduced uncertainty in
advertising; the search engine can improve the advertisers' loyalty as well as
obtain a stable and increased expected revenue over time. Since the proposed ad
option can be implemented in conjunction with the existing keyword auctions,
the option price and corresponding fixed CPCs must be set such that there is no
arbitrage between the two markets. Option pricing methods are discussed and our
experimental results validate the development. Compared to keyword auctions, a
search engine can have an increased expected revenue by selling an ad option.Comment: Chen, Bowei and Wang, Jun and Cox, Ingemar J. and Kankanhalli, Mohan
S. (2015) Multi-keyword multi-click advertisement option contracts for
sponsored search. ACM Transactions on Intelligent Systems and Technology, 7
(1). pp. 1-29. ISSN: 2157-690
Pricing average price advertising options when underlying spot market prices are discontinuous
Advertising options have been recently studied as a special type of
guaranteed contracts in online advertising, which are an alternative sales
mechanism to real-time auctions. An advertising option is a contract which
gives its buyer a right but not obligation to enter into transactions to
purchase page views or link clicks at one or multiple pre-specified prices in a
specific future period. Different from typical guaranteed contracts, the option
buyer pays a lower upfront fee but can have greater flexibility and more
control of advertising. Many studies on advertising options so far have been
restricted to the situations where the option payoff is determined by the
underlying spot market price at a specific time point and the price evolution
over time is assumed to be continuous. The former leads to a biased calculation
of option payoff and the latter is invalid empirically for many online
advertising slots. This paper addresses these two limitations by proposing a
new advertising option pricing framework. First, the option payoff is
calculated based on an average price over a specific future period. Therefore,
the option becomes path-dependent. The average price is measured by the power
mean, which contains several existing option payoff functions as its special
cases. Second, jump-diffusion stochastic models are used to describe the
movement of the underlying spot market price, which incorporate several
important statistical properties including jumps and spikes, non-normality, and
absence of autocorrelations. A general option pricing algorithm is obtained
based on Monte Carlo simulation. In addition, an explicit pricing formula is
derived for the case when the option payoff is based on the geometric mean.
This pricing formula is also a generalized version of several other option
pricing models discussed in related studies.Comment: IEEE Transactions on Knowledge and Data Engineering, 201
Quantum computational finance: martingale asset pricing for incomplete markets
A derivative is a financial security whose value is a function of underlying
traded assets and market outcomes. Pricing a financial derivative involves
setting up a market model, finding a martingale (``fair game") probability
measure for the model from the given asset prices, and using that probability
measure to price the derivative. When the number of underlying assets and/or
the number of market outcomes in the model is large, pricing can be
computationally demanding. We show that a variety of quantum techniques can be
applied to the pricing problem in finance, with a particular focus on
incomplete markets. We discuss three different methods that are distinct from
previous works: they do not use the quantum algorithms for Monte Carlo
estimation and they extract the martingale measure from market variables akin
to bootstrapping, a common practice among financial institutions. The first two
methods are based on a formulation of the pricing problem into a linear program
and are using respectively the quantum zero-sum game algorithm and the quantum
simplex algorithm as subroutines. For the last algorithm, we formalize a new
market assumption milder than market completeness for which quantum linear
systems solvers can be applied with the associated potential for large
speedups. As a prototype use case, we conduct numerical experiments in the
framework of the Black-Scholes-Merton model.Comment: 31 pages, 6 figure
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