论文标题
估计现实世界概率：前瞻性行为框架
Estimating real-world probabilities: A forward-looking behavioral framework
论文作者
论文摘要
我们表明，解散情感引起的偏见与基本期望显着提高了概率预测的准确性和一致性。使用1994年至2017年的数据，我们分析了15个随机模型和风险优先组合，并且在所有可能的情况下，简单的行为转化都会带来可观的预测收益。我们的结果在不同的评估方法，风险优先假设和情感校准方面非常强大，这表明行为效应可有效地用于预测资产价格。进一步的分析证实，我们的现实密度优于重新校准，以避免过去的错误并改善从期权价格动态估计风险厌恶的预测模型。
We show that disentangling sentiment-induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, we analyze 15 stochastic models and risk-preference combinations and in all possible cases a simple behavioral transformation delivers substantial forecast gains. Our results are robust across different evaluation methods, risk-preference hypotheses and sentiment calibrations, demonstrating that behavioral effects can be effectively used to forecast asset prices. Further analyses confirm that our real-world densities outperform densities recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.
