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首页> 外文期刊>Journal of Economic Dynamics and Control >Option prices under Bayesian learning: implied volatility dynamics and predictive densities
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Option prices under Bayesian learning: implied volatility dynamics and predictive densities

机译：贝叶斯学习下的期权价格：隐含波动率动态和预测密度

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This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolve on a binomial lattice with unknown but recursively updated probabilities we derive closed-form pricing formulas for European options. Learning is found to generate asymmetric skews in the implied volatility surface and systematic patterns in the term structure of option prices. Data on S&P 500 index option prices is used to back out the parameters of the underlying learning process and to predict the evolution in the cross-section of option prices. The proposed model leads to lower out-of-sample forecast errors and smaller hedging errors than a variety of alternative option pricing models, including Black-Scholes and a GARCH model.
机译：本文表明，贝叶斯学习效应可以解释布莱克和斯科尔斯期权定价模型的许多经验偏差。在均衡模型的背景下，股利新闻在具有未知但递归更新的概率的二项式网格上演化，我们得出了欧洲期权的封闭式定价公式。发现学习会在隐含的波动表面和期权价格期限结构的系统模式中产生不对称的偏斜。标普500指数期权价格的数据用于回溯基础学习过程的参数，并预测期权价格横截面的变化。与包括Black-Scholes和GARCH模型在内的多种备选期权定价模型相比，所提出的模型导致较低的样本外预测误差和较小的对冲误差。

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