A lattice framework for pricing display advertisement options with the stochastic volatility underlying model

Advertisement (abbreviated ad) options are a recent development in online advertising. Simply, an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre-specified price on a specific delivery date. Such a structure provides advertisers with more flexibility of their guaranteed deliveries. The valuation of ad options is an important topic and previous studies on ad options pricing have been mostly restricted to the situations where the underlying prices follow a geometric Brownian motion (GBM). This assumption is reasonable for sponsored search; however, some studies have also indicated that it is not valid for display advertising. In this paper, we address this issue by employing a stochastic volatility (SV) model and discuss a lattice framework to approximate the proposed SV model in option pricing. Our developments are validated by experiments with real advertising data: (i) we find that the SV model has a better fitness over the GBM model; (ii) we validate the proposed lattice model via two sequential Monte Carlo simulation methods; (iii) we demonstrate that advertisers are able to flexibly manage their guaranteed deliveries by using the proposed options, and publishers can have an increased revenue when some of their inventories are sold via ad options.Comment: Bowei Chen and Jun Wang. A lattice framework for pricing display advertisement options with the stochastic volatility underlying model. Electronic Commerce Research and Applications, 2015, Volume 14, Issue 6, pages 465-479, ISSN: 1567-422

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

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

Valuation of Phoenics

This work presents theoretical background for diverse valuation methods, with special attention paid to the real options method and its advantages over traditional methods for valuation of young entities. Additionally, it presents a case study of a start-up company, the value of which is found using diverse methods. The main result shows that the value of the company obtained with the real options approach is much higher than the one found with traditional discounted cash flow (DCF) method. Moreover, it shows that the difference in the obtained valuations leads to different strategic decisions: according to DCF certain projects should not be undertaken, whereas according to the real options approach the company should expand its operations.Esta dissertação apresenta uma base teórica para diversos métodos de avaliação, com especial atenção para o método de opções reais e as suas vantagens em relação aos métodos tradicionais de avaliação de entidades jovens. Além disso, apresenta um case study de uma empresa start-up, cujo o seu valor é encontrado usando diversos métodos. O resultado principal mostra que o valor da empresa obtido com a utilização de opções reais é muito maior do que o encontrado com o método tradicional de fluxos de caixa descontados (DCF). Também mostra que a diferença entre as valorizações obtidas, leva a diferentes decisões estratégicas: de acordo com DCF determinados projectos não devem ser realizados, no entanto segundo as opções reais, a empresa deve expandir suas operaçõe

Financial Methods for Online Advertising

Online advertising, a form of advertising that reaches consumers through the World Wide Web, has become a multi-billion dollar industry. Using the state of the art computing technologies, online auctions have become an important sales mechanism for automating transactions in online advertising markets, where advertisement (shortly ad) inventories, such as impressions or clicks, are able to be auctioned off in milliseconds after they are generated by online users. However, with providing non-guaranteed deliveries, the current auction mechanisms have a number of limitations including: the uncertainty in the winning payment prices for buyers; the volatility in the seller’s revenue; and the weak loyalty between buyer and seller. To address these issues, this thesis explores the methods and techniques from finance to evaluate and allocate ad inventories over time and to design new sales models. Finance, as a sub-field of microeconomics, studies how individuals and organisations make decisions regarding the allocation of resources over time as well as the handling of risk. Therefore, we believe that financial methods can be used to provide novel solutions to the non-guaranteed delivery problem in online advertising. This thesis has three major contributions. We first study an optimal dynamic model for unifying programmatic guarantee and real-time bidding in display advertising. This study solves the problem of algorithmic pricing and allocation of guaranteed contracts. We then propose a multi-keyword multi-click ad option. This work discusses a flexible way of guaranteed deliveries in the sponsored search context, and it’s evaluation is under the no arbitrage principle and is based on the assumption that the underlying winning payment prices of candidate keywords for specific positions follow a geometric Brownian motion. However, according to our data analysis and other previous research, the same underlying assumption is not valid empirically for display ads. We therefore study a lattice framework to price an ad option based on a stochastic volatility underlying model. This research extends the usage of ad options to display advertising in a more general situation

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

While page views are often sold instantly through real-time auctions when users visit Web pages, they can also be sold in advance via guaranteed contracts. In this paper, we combine guaranteed and spot markets in display advertising, and present a dynamic programming model to study how a media seller should optimally allocate and price page views between guaranteed contracts and advertising auctions. This optimisation problem is challenging because the allocation and pricing of guaranteed contracts endogenously affects the expected revenue from advertising auctions in the future. We take into consideration several distinct characteristics regarding the media buyers’ purchasing behaviour, such as risk aversion, stochastic demand arrivals, and devise a scalable and efficient algorithm to solve the optimisation problem. Our work is one of a few studies that investigate the auction-based posted price guaranteed contracts for display advertising. The proposed model is further empirically validated with a display advertising data set from a UK supply-side platform. The results show that the optimal pricing and allocation strategies from our model can significantly increase the media seller’s expected total revenue, and the model suggests different optimal strategies based on the level of competition in advertising auctions