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.

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.

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.

Modeling of a stereo vision system using a genetic algorithm based fuzzy linear regression

In this paper a fuzzy linear regression (FLR) model integrated with a genetic algorithm (GA) is proposed. The proposed GA-FLR model is applied to modeling of a stereo vision system. A set of empirical data from stereo vision object measurement is collected based on the full factorial design technique. Three regression models, namely ordinary least-squares regression (OLS), FLR, and GA-FLR, are developed, and with their performances compared. The results show that the proposed GA-FLR model performs better than OLS and FLR in modeling of a stereo vision system.

A new approach based on support vector machine for solving stochastic optimization

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.