We present the first calibration of quantum decision theory (QDT) to an empirical data set. The data comprise 91 choices between two lotteries (two "prospects") presented in 91 random pairs made by 142 subjects offered at two separated times. First, we quantitatively account for the fraction of...

We investigate what it means for one act to be more ambiguous than another. The question is evidently analogous to asking what makes one prospect riskier than another, but beliefs are neither objective nor representable by a unique probability. Our starting point is an abstract class of...

We consider optimal stopping problems for ambiguity averse decision makers with multiple priors. In general, backward induction fails. If, however, the class of priors is time-consistent, we establish a generalization of the classical theory of optimal stopping. To this end, we develop first...

We analyze several exotic options of American style in a multiple prior setting and study the optimal exercise strategy from the perspective of an ambiguity averse buyer in a discrete time model of Cox-Ross-Rubinstein style. The multiple prior model relaxes the assumption of a known distribution...

We develop a theory of optimal stopping problems under ambiguity in continuous time. Using results from (backward) stochastic calculus, we characterize the value function as the smallest (nonlinear) supermartingale dominating the payoff process. For Markovian models, we derive an adjusted...

I develop a stochastic growth model with production where there is a hidden state governing productivity growth regimes, and the hidden state evolves according to a Markov chain. Economic agents learn about the hidden state and display ambiguity aversion in the spirit of Klibanoff et al. (2005)....

We examine a production-based asset pricing model with an unobservable mean growth rate ollowing a two-state Markov chain and with an ambiguity averse representative agent. Our model requires a low coefficient of relative risk aversion to produce: (i) a high equity premium and volatile equity...

We confront the generalized recursive smooth ambiguity aversion preferences of Klibanoff, Marinacci, and Mukerji (2005, 2009) with data using Bayesian methods introduced by Gallant and McCulloch (2009) to close two existing gaps in the literature. First, we use macroeconomic and financial data...

We present a continuous-time agency model under mean-volatility joint ambiguity uncertainties, where both the principal and agent exhibit Gilboa-Schmeidler's extreme ambiguity aversion. For this, we extend the martingale method well known in the agency literature, by allowing not only the mean...

The concept of model uncertainty is one of increasing importance in the field of Mathematical Finance. The main goal of this work is to explore model uncertainty in the specific area of algorithmic and high frequency trading. From a behavioural perspective, model uncertainty naturally leads to...