Prefrontal connections express individual differences in intrinsic resistance to trading off honesty values against economic benefits.

Individuals differ profoundly when they decide whether to tell the truth or to be dishonest, particularly in situations where moral motives clash with economic motives, i.e., when truthfulness comes at a monetary cost. These differences should be expressed in the decision network, particularly in prefrontal cortex. However, the interactions between the core players of the decision network during honesty-related decisions involving trade-offs with economic costs remain poorly understood. To investigate brain connectivity patterns associated with individual differences in responding to economic costs of truthfulness, we used functional magnetic resonance imaging and measured brain activations, while participants made decisions concerning honesty. We found that in participants who valued honesty highly, dorsolateral and dorsomedial parts of prefrontal cortex were more tightly coupled with the inferior frontal cortex when economic costs were high compared to when they were low. Finer-grained analysis revealed that information flow from the inferior frontal cortex to the dorsolateral prefrontal cortex and bidirectional information flow between the inferior frontal cortex and dorsomedial prefrontal cortex was associated with a reduced tendency to trade off honesty for economic benefits. Our findings provide a novel account of the neural circuitry that underlies honest decisions in the face of economic temptations.

Model risk for European-style stock index options.

In empirical modeling, there have been two strands for pricing in the options literature, namely the parametric and nonparametric models. Often, the support for the nonparametric methods is based on a benchmark such as the Black-Scholes (BS) model with constant volatility. In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network models. Our choice for FNN models is due to their well-studied universal approximation properties of an unknown function and its partial derivatives. Since the partial derivatives of an option pricing formula are risk pricing tools, an accurate estimation of the unknown option pricing function is essential for pricing and hedging. Our findings indicate that FNN models offer themselves as robust option pricing tools, over their sophisticated parametric counterparts in predictive settings. There are two routes to explain the superiority of FNN models over the parametric models in forecast settings. These are nonnormality of return distributions and adaptive learning.