This paper models the incentive misalignment between firm owners and their employees as a barrier to technology diffusion, which is a critical yet understudied feature, in technology adoption settings. To do so, we consider a general continuous-time optimal stopping framework with the...

In this paper, we develop a model for on-demand telemedicine platform operations that focuses on managing multi-specialty, online-based care in the presence of general and specialized care demand interactions, as well as multiple physician classes. Although this type of demand interaction is...

In this paper, we propose a general continuous-time stochastic-modeling framework where a financial firm offers incentive bonuses to a team of employees, who would thus exert effort to reduce operational risk losses. We characterize employees' Nash equilibrium efforts and the firm's optimal...

This paper proposes a mutually exciting discrete-time stochastic model to capture two essential features underlying the bank-customer behavior process---the dependence on the past behavior (i.e., path-dependence) and the behavioral interdependence between deposit and withdrawal activities (i.e.,...

The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services. However, the literature has focused mainly on the...

In this short paper, we study the asymptotics for the price of call options for very large strikes and put options for very small strikes. The stock price is assumed to follow the Black-Scholes models. We analyze European, Asian, American, Parisian and perpetual options and conclude that the...

One popular approach to model the limit order books dynamics of the best bid and ask at level-1 is to use the reduced-form diffusion approximations. It is well known that the biggest contributing factor to the price movement is the imbalance of the best bid and ask. We investigate the data of...

Dual risk models are popular for modeling a venture capital or high tech company, for which the running cost is deterministic and the profits arrive stochastically over time. Most of the existing literature on dual risk models concentrated on the optimal dividend strategies. In this paper, we...

We develop approximate estimation methods for exponential random graph models (ERGMs), whose likelihood is proportional to an intractable normalizing constant. The usual approach approximates this constant with Monte Carlo simulations, however convergence may be exponentially slow. We propose a...

In recent literature, a new class of unbiased Monte Carlo estimators have been proposed, which is based on truncating a telescopic representation of the expectation of a functional of the stochastic process at an independent random level. The generality of the method lies in that it can...