Calibration and simulation are a critical, but time-consuming process in modern computational finance applications. Through an example Monte Carlo simulation of interest rate models for counterparty credit risk analysis, Kevin highlights best practices for creating and calibrating models, performing simulations, and optimizing code for performance using MATLAB. He shows how single-factor and multifactor models can be calibrated to both current market data and historical data using Kalman filter and state-space modeling and simulates a portfolio of interest rate instruments. He concludes with discussion on how to deploy MATLAB models into enterprise applications for on-demand risk analysis and reporting.