Schittenkopf C, Dorffner G
Austrian Research Institute for Artificial Intelligence, 1010 Vienna, Austria.
IEEE Trans Neural Netw. 2001;12(4):716-25. doi: 10.1109/72.935085.
One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a new semi-nonparametric approach to risk-neutral density extraction from option prices, which is based on an extension of the concept of mixture density networks. The central idea is to model the shape of the risk-neutral density in a flexible, nonlinear way as a function of the time horizon. Thereby, stylized facts such as negative skewness and excess kurtosis are captured. The approach is applied to a very large set of intraday options data on the FTSE 100 recorded at LIFFE. It is shown to yield significantly better results in terms of out-of-sample pricing accuracy in comparison to the basic and an extended Black-Scholes model. It is also significantly better than a more elaborate GARCH option pricing model which includes a time-dependent volatility process. From the perspective of risk management, the extracted risk-neutral densities provide valuable information for value-at-risk estimations.
金融领域的核心目标之一是找到更好的模型来对诸如看涨期权和看跌期权等金融衍生品进行定价和套期保值。我们提出了一种从期权价格中提取风险中性密度的新半非参数方法，该方法基于混合密度网络概念的扩展。核心思想是以灵活的非线性方式将风险中性密度的形状建模为时间范围的函数。由此，捕捉到了诸如负偏度和超额峰度等典型事实。该方法应用于在伦敦国际金融期货交易所（LIFFE）记录的关于富时100指数的大量日内期权数据。结果表明，与基本的和扩展的布莱克-斯科尔斯模型相比，该方法在样本外定价准确性方面产生了显著更好的结果。它也明显优于一个更复杂的包含时间依赖波动率过程的广义自回归条件异方差（GARCH）期权定价模型。从风险管理的角度来看，提取的风险中性密度为风险价值估计提供了有价值的信息。
