Travel time derivatives are financial instruments that derive their value from road travel times, serving as an underlying asset that cannot be directly traded. Within the transportation domain, these derivatives are proposed as a more comprehensive approach to value pricing. They enable road pricing based not only on the level of travel time but also its volatility. In the financial market, travel time derivatives are introduced as innovative hedging instruments to mitigate market risk, particularly in light of recent stress experienced by the crypto market and traditional banking sector. The paper focuses on three main aspects: (1) the motivation behind the introduction of these derivatives, driven by the demand for hedging; (2) exploring the potential market for these instruments; and (3) delving into the product design and pricing schemes associated with them. The pricing schemes are devised by utilizing real-time travel time data captured by sensors. These data are modeled using Ornstein-Uhlenbeck processes and, more broadly, continuous time autoregressive moving average (CARMA) models. The calibration of these models is achieved through a hidden factor model, which describes the dynamics of travel time processes. The risk-neutral pricing principle is then employed to determine the prices of the derivatives, employing well-designed procedures to identify the market value of risk.

翻译：通行时间衍生品是一种以道路通行时间为基础资产（该基础资产无法直接交易）的金融工具。在交通领域，此类衍生品被提出作为更全面的价值定价方法，不仅能依据通行时间水平进行道路定价，还可考虑其波动性。在金融市场中，通行时间衍生品被引入作为创新型对冲工具以缓解市场风险，尤其是在加密货币市场与传统银行业近期面临压力背景下。本文聚焦三大核心方面：（1）对冲需求驱动下引入此类衍生品的动因；（2）探索此类工具的潜在市场；（3）深入分析其产品设计与定价方案。定价方案利用传感器捕获的实时通行时间数据构建，通过奥恩斯坦-乌伦贝克过程及更广义的连续时间自回归滑动平均（CARMA）模型对数据进行建模。模型校准采用隐因子模型描述通行时间过程的动态特征，并基于风险中性定价原则，通过精心设计的风险市场价格识别程序确定衍生品价格。