Click here for our detailed Basel II Brochure and please contact us at admin@realoptionsvaluation.com for more information on our new Basel II Modeling Toolkit and how we can assist!

The Basel II Modeling Toolkit (with the Premium Edition) provides the following analytical software and modeling toolkits:

· BASEL II MODELING TOOLKIT SOFTWARE

· RISK SIMULATOR SOFTWARE

· REAL OPTIONS SLS SOFTWARE

BASEL II MODELING TOOLKIT

This toolkit comprises over 100+ analytical models and example spreadsheets covering the areas of risk analysis and IRB requirements in the New Basel II Accord:

· Credit Analysis – credit premium analysis, external credit and market-based credit analysis, and internal credit rating models.
· Debt Analysis – asset-equity parity models, Cox models, stochastic Merton models, and Vasicek structural partial differential models, to determine the value of risky debt, yields, and mean-reverting behaviors of interest rates, as well as applying real options analysis to determine the value of risky debt and required returns.
· Stochastic Forecasting – Box-Jenkins ARIMA econometric models, time-series forecasting, nonlinear extrapolations, multivariate regressions and stochastic processes (e.g., mean-reverting interest rate forecasts).
· Operational Risk – Queuing models and operational risk analysis.
· Optimization – Discrete, Continuous, Dynamic, and Stochastic optimization models are used to determine the most efficient and effective allocation of credit risk portfolios, Values at Risk determination, asset allocation, investment opportunities, and to determine the simultaneous two-factor solutions to the Merton external credit risk profile of a company.
· Probability of Default Models – the toolkit includes internal, external, market, and empirical credit default models. We apply options-based modeling to determine the default credit risk, stochastic default risk, empirical probability of default and distance to default.
· Risk Hedging Models – Delta hedging models, simultaneous Delta-Gamma hedging models, foreign exchange hedging models, and others are provided in the toolkit.
· Sensitivity Analysis – Options Greeks as well as bond-debt first-order durations and second-order convexities are computed.
· Valuation – foreign currency, foreign-based equity and commodity-based options and forwards are computed to determine the value of risk hedging. Other models like perpetual derivatives and exotic options are included.
· Value at Risk – Covariance static models in Value at Risk as well as using Monte Carlo simulation and historical customized simulation techniques coupled with internal optimization routines (credit adequacy requirements in Basel II). 
· Yield Curve Modeling – Interpolation models, extrapolation models, mean-reverting, BIM and stochastic Vasicek term structure models are included. With the assistance of Risk Simulator, we can also simulate mean-reverting, jump-diffusion, and random walk forecasts and valuations of prices and interest rates.

RISK SIMULATOR: Monte Carlo Risk Simulation Module

· Simulation. Quantify risk through simulating hundreds of thousands of trials of events or variables, risk parameters, as well as the resulting probabilities of occurrence (impact severity or frequency of impacts). There are over 25 probability distributions available in Risk Simulator, including the ability to customize and build your own distribution. Historical simulation and bootstrap simulation to determine the relative risk profiles, and resulting forecast statistics. Value at Risk (Capital Risk Requirements) can be determined. 

· Identify the Key Risk Indicators and determine which parameters are critical to the overall risk to the enterprise. Measures the contribution to variance of each KRI to the entire enterprise after accounting for their dynamic and correlated relationships.
· Distributional Fitting. Thousands of data points can be converted into a single probability distribution, providing a robust and concise method to model credit, operational, and market risk situations. 
· Sensitivity Analysis. This is critical in determining which precedent variables drive the outcome of the analysis the most, and can be run either in a deterministic or dynamic correlated mode. 
· Maximum Likelihood Models. Maximum likelihood iterative and internal optimization procedures are used to model binary response variables (the dependent variable is binary, taking on the values of 0 or 1). This is a key discriminant analysis with multiple uses (e.g., to determine if a credit line or person will default on a loan given the company’s assets, asset volatility, or the person’s age, education level, years at a job, etc).
· Stochastic Parameters Estimation. This tool is used to determine the correct input parameters in modeling yield curves, term structures, and other macroeconomic variables.
· Statistical Analysis Tool. This tool is used to describe the statistical characteristics of your data before performing detailed analyses.
· Data Diagnostic Tool. These tests are vital before starting with any types of forecasting or data analysis procedures. Each test comes complete with an easy-to-understand detailed report so that it does not take a trained econometrician or statistician to understand and interpret the results. Tests include, among others, Heteroskedasticity, Multicollinearity, Micronumerosity, Nonlinearity, Outliers, Autocorrelation, Partial Autocorrelation, Distributive Lag, Normality and Sphericity, Nonstationarity, Stochastic Characteristics, Linear and Nonlinear Correlations, Variance Inflation Factors, Visual Charts.

RISK SIMULATOR: Optimization Module  

· Runs static optimization as well as dynamic simulation-optimization routines, and pure stochastic optimization runs. Efficient asset allocation in a portfolio, project selection, credit portfolio optimization, determination of an external market-based credit model, optimal credit grants & other risk-based analysis.  
· Efficiently and effectively allocate capital to find the maximum returns subject to the least risk, and subject to budget and resource constraints to obtain portfolios that are the best and most optimal given the circumstances.
· Apply cross-correlations among operational risk parameters as well as determine the statistical significance of the correlations and decide which are significant contributors to overall enterprise-wide operational risk metrics.

RISK SIMULATOR: Forecasting Module

From basic multivariate regressions to advanced econometric modeling and Box-Jenkins ARIMA as well as stochastic processes (mean-reversion process, jump-diffusion, random walks) are available to model and forecast economic variables (interest rates, inflation, foreign exchange, and others). These models have their inputs calibrated using the data diagnostic tool, to determine the level of data stationarity and stochastic nature of the data variable under study. Mean-reverting term structure models can be adapted and modeled.

REAL OPTIONS SLS: Option Analysis Module

· American, Asian, Bermudan, European and Mixed Options with vesting and blackout periods applying closed-form models as well as binomial, trinomial, and multinomial lattices.
· Real options such as options to expand, execute, abandon, contract, switch, barriers, multi-phased sequential compound options, multiple underlying asset options, and any types of mixed-and-match customizable options. The analysis can be combined with Risk Simulator and the Basel II Modeling Toolkit to create a comprehensive and powerful set of risk, decision, and strategic analytics.
· In the banking industry, real options can be used to value contractual terms (barrier options and other types of options), guaranteed payouts (option to execute), multiple issue grants (sequential compound options), and valuing an enterprise or projects within a company after accounting for its ability to diversify (options to expand), liquidate (abandonment option), and many other applications.

TRAINING AND CONSULTING

Advanced analytical tools such as the Basel II Toolkit, Risk Simulator and Real Options SLS software might be easy to use but may get the analyst in trouble if used inappropriately. Sufficient theoretical understanding coupled with pragmatic application experience is vital; therefore training is critical. We offer 1-week and 2-week Risk Analysis seminars focused on hands-on software training. Topics covered include the basics of risk and uncertainty, credit risk, market and operational risk analysis. All these seminars use Monte Carlo simulation, stochastic modeling, real options analysis, portfolio optimization and are based on the results of a customized consulting project performed for your institution, using the same models and tools we used to solve various banking risk issues. We offer the Certified in Risk Management (CRM) and Certified in Senior Credit Risk Management (SCRM) training and certifications.

SUPPORT MATERIALS AND EXPERTISE

· 12 books on risk analysis, modeling risk, credit and banking risk analysis written by the company’s strategic partners of Real Options Valuation, Inc. in banking risk analysis and creators of the software programs
· Training DVD on risk analysis (simulation, forecasting, optimization, real options, and applied business statistics)
· Live customizable 1 and 2 week training courses on credit risk, banking risk, risk analysis, simulation, forecasting, optimization, and real options
· Detailed user manuals, help files, and an extensive library of example files, and 3 software applications ready to be deployed
· Live project consultants who are subject matter experts in risk analysis and credit/operational/market risk specialists, book authors, professors at universities and consulting practitioners