Asset pricing under smooth ambiguity in continuous time
研究了连续时间环境下平滑模糊性投资者偏好的资产定价含义，发现投资者对隐藏马尔可夫状态的模糊性会改变其决策和均衡资产价格，并利用递归滤波和HJB方法求解修正后的决策问题。
Abstract We study asset pricing implications of a revealing and tractable formulation of smooth ambiguity investor preferences in a continuous-time environment. Investors do not observe a hidden Markov state and instead make inferences about this state using past data. We show that ambiguity about this hidden state distribution alters investor decisions and equilibrium asset prices. Our continuous-time formulation allows us to apply recursive filtering and Hamilton–Jacobi–Bellman methods to solve the modified decision problem. Using such methods, we show how characterizations of portfolio allocations and local uncertainty-return tradeoffs change when investors are ambiguity-averse.