英文标题：
《A lattice framework for pricing display advertisement options with the
  stochastic volatility underlying model》
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作者：
Bowei Chen and Jun Wang
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最新提交年份：
2015
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英文摘要：
  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.
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中文摘要：
广告（缩写广告）选项是在线广告的最新发展。简单地说，广告选项是一种首看合同，在该合同中，出版商或搜索引擎授予广告客户在特定交付日期以预先指定的价格从特定广告时段购买印象或点击的权利，但没有义务。这样的结构为广告商提供了更大的保证交付的灵活性。广告期权的定价是一个重要的课题，以往对广告期权定价的研究大多局限于标的价格服从几何布朗运动（GBM）的情况。这种假设对于赞助搜索是合理的；然而，一些研究也表明，它不适用于展示广告。在本文中，我们通过使用随机波动率（SV）模型来解决这个问题，并讨论了在期权定价中近似所提出的SV模型的晶格框架。通过真实广告数据的实验验证了我们的发展：（i）我们发现SV模型比GBM模型具有更好的拟合度；（ii）我们通过两种顺序蒙特卡罗模拟方法验证了所提出的晶格模型；（iii）我们证明，广告商能够通过使用建议的选项灵活地管理他们的保证交付，并且当出版商的部分存货通过广告选项出售时，他们可以增加收入。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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