论文标题
温度导数评估的随机波动率模型
A stochastic volatility model for the valuation of temperature derivatives
论文作者
论文摘要
本文为温度开发了一种新的随机波动率模型，这是Benth and Benth（2007）提出的Ornstein-Uhlenbeck模型的自然扩展。该模型可以在保持障碍的同时更加保守。我们提供了一种基于条件最小二乘的方法，以估计每日数据的参数，并在八个主要的欧洲城市上估算我们的模型。然后，我们展示如何通过蒙特卡洛和傅立叶变换技术有效地计算天气衍生物的平均收益。该新模型允许更好地评估与温度波动有关的风险。
This paper develops a new stochastic volatility model for the temperature that is a natural extension of the Ornstein-Uhlenbeck model proposed by Benth and Benth (2007). This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then show how to calculate efficiently the average payoff of weather derivatives both by Monte-Carlo and Fourier transform techniques. This new model allows to better assess the risk related to temperature volatility.
