Disagreements with noisy signals and asset pricing

Wang, Hailong; Hu, Duni; Cheng, Fengchao

doi:10.1016/j.najef.2019.101062

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…There are several possible strategies to incorporate the learning process into the current modeling framework. The filtering formalism 89,90 can be incorporated into the model as well. Another possibility is to enhance the current model with the Bayesian updating formalism harmonizing with stochastic optimal control problems 91 …”

Section: Discussionmentioning

confidence: 99%

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

Yoshioka

Yaegashi

Tsujimura

et al. 2020

Appl Stoch Models Bus & Ind

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Planning a cost‐efficient monitoring policy of stochastic processes arises from many industrial problems. We formulate a simple discrete‐time monitoring problem of continuous‐time stochastic processes with its applications to several industrial problems. A key in our model is a doubling trick of the variables, with which we can construct an algorithm to solve the problem. The cost‐efficient monitoring policy balancing between the observation cost and information loss is governed by an optimality equation of a fixed point type, which is solvable with an iterative algorithm based on the Feynman‐Kac formula. This is a new linkage between monitoring problems and mathematical sciences. We show regularity results of the optimization problem and present a numerical algorithm for its approximation. A problem having model ambiguity is presented as well. The presented model is applied to problems of environment, ecology, and energy, having qualitatively different target stochastic processes with each other.

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Section: Discussionmentioning

confidence: 99%