Monte Carlo simulations and capital structure research

The evolution of the debt ratio under alternative types of managerialbehavior can generate non-standard leverage processes. This createsproblems for statistical inference in empirical capital structure research. We argue in this paper that when the data generating process is not standard, a useful way to evaluate the appropriateness of inferences and the empirical methodology is via Monte Carlo simulations that mimic the data generating process under alternative assumptions about managerial behavior. We illustrate with several examples.

Monte Carlo calculations of the free energy of binary SII hydrogen clathrate hydrates for identifying efficient promoter molecules

The thermodynamics of binary sII hydrogen clathrates with secondary guest molecules is studied with Monte Carlo simulations. The small cages of the sII unit cell are occupied by one H2 guest molecule. Different promoter molecules entrapped in the large cages are considered. Simulations are conducted at a pressure of 1000 atm in a temperature range of 233?293 K. To determine the stabilizing effect of different promoter molecules on the clathrate, the Gibbs free energy of fully and partially occupied sII hydrogen clathrates are calculated. Our aim is to predict what would be an efficient promoter molecule using properties such as size, dipole moment, and hydrogen bonding capability. The gas clathrate configurational and free energies are compared. The entropy makes a considerable contribution to the free energy and should be taken into account in determining stability conditions of binary sII hydrogen clathrates.

Credit risk of interest rate swaps : a comparative study of CIR and Monte Carlo simulation approach

This paper compares the credit risk profile for two types of model, the Monte Carlo model used in the existing literature, and the Cox, Ingersoll and Ross (CIR) model. Each of the profiles has a concave or hump-backed shape, reflecting the amortisation and diffusion effects. However, the CIR model generates significantly different results. In addition, we consider the sensitivity of these models of credit risk to initial interest rates, volatility, maturity, kappa and delta. The results show that the sensitivities vary across the models, and we explore the meaning of that variation.

A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.

Simulations of structure and transport in polymer electrolytes

Simulations implementing both Monte Carlo (MC) and molecular dynamics (MD) techniques were used to explore various aspects of polymer electrolytes. Evidence is presented to support the conclusion that collective behavior of ions determines much of the behavior of these complex materials. Simple theories attributing ion transport to either single ions or clusters of three ions are inadequate to explain ion transport behavior; in particular, the Nernst-Einstein relation commonly used to discuss polymer electrolytes is almost certainly quantitatively inappropriate for these materials.

Atomically resolved tomography to directly inform simulations for structure-property relationships

Microscopy encompasses a wide variety of forms and scales. So too does the array of simulation techniques developed that correlate to and build upon microstructural information. Nevertheless, a true nexus between microscopy and atomistic simulations is lacking. Atom probe has emerged as a potential means of achieving this goal. Atom probe generates three-dimensional atomistic images in a format almost identical to many atomistic simulations. However, this data is imperfect, preventing input into computational algorithms to predict material properties. Here we describe a methodology to overcome these limitations, based on a hybrid data format, blending atom probe and predictive Monte Carlo simulations. We create atomically complete and lattice-bound models of material specimens. This hybrid data can then be used as direct input into density functional theory simulations to calculate local energetics and elastic properties. This research demonstrates the role that atom probe combined with theoretical approaches can play in modern materials engineering.