Abstract: System, method and framework for generating scenarios used in risk management applications. The present invention is based on a generic framework that provides levels of abstraction, segregates risk factors and models, and structures a scenario generation process.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic credit risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic credit risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: A risk management system and method provides for the establishment of dynamic portfolios, whose evolution over time is defined by one or more rules. Each dynamic portfolio can have instruments added and removed over time in accordance with Trade Managers as a result of evaluation of the user-defined rules which can be dependent upon various attributes, including time, portfolio contents, risk factor values, risk values and other information. Such dynamic portfolios can be used to analyze risk associated with settlement, liquidity and/or collateral management issues, to name a few. Also, a user can define multiple candidate trading strategies, each implemented in one or more Trade Managers, and the user can then analyze the effectiveness of the candidate strategies, before adopting one.

Abstract: The present invention is directed to a generic, object-oriented library of generators and operations on generators. The library of generators and other objects provides a set of reusable components that can be used in the development of other components required for new risk management generation models. Using basic simple objects predefined in the library and the operations on these objects, complicated objects required for a new risk management generation models may be built more efficiently and easily.