How IMSL Is Used in Financial Applications

From asset management to investment banking, financial organizations around the world rely on IMSL Numerical Libraries for advanced data analysis and visualization.

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Financial Forecasting and Modeling

Create data-driven forecasting and modeling for equities, currencies, and commodities.

Portfolio and Trading Optimization

Optimize your trading and portfolio strategies by identifying patterns, opportunities, and limitations.

Financial Risk Management

See the best, worst, and most likely outcomes to help inform financial decision making.

Interest and Exchange Rate Modeling

Accurately model interest and exchange rates for more informed financial strategies.

Fixed Income Analysis

Analyze interest rate risk, credit risk, and expected price behavior for security trading.

Options and Derivatives Pricing

Efficiently calculate options and derivatives pricing for timely and relevant trading insights.

IMSL for Financial Forecasting and Financial Modeling

With financial forecasting algorithms like GARCH, ARMA, Auto_ARIMA, and advanced forecasting techniques like Feed Forward Neural Networks, quantitative analysts and researches can create data-driven forecasting for equities, fixed income, currency, and commodities.

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Market Volatility Forecasting

With IMSL financial algorithms, quantitative analysts and researchers can use data collection, processing, visualization, and modeling to predict market volatility.

Financial Performance Modeling

Quickly add application functionality for prediction, simulation, optimization, and other financial modeling techniques with IMSL financial modeling algorithms.

Risk Modeling for Success

What risk modeling methods are used today? How can you effectively use them?

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IMSL for Portfolio Optimization, Trading Optimization, and Financial Risk Management

IMSL libraries feature financial algorithms, functions, and techniques that help quantitative analysts deliver high-performance, risk-averse, and high-ROI trading portfolio management strategies.

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Portfolio Optimization

With linear, non-linear, quadratic programming, and other options within the IMSL libraries, asset managers and quantitative analysts can quickly develop versatile portfolio optimization applications.

Financial Risk Management

IMSL financial risk management algorithms can calculate information about range of outcomes, such as best / worst-case, the chances of reaching target goals, and the most likely outcomes.

Trading Strategy Optimization

IMSL financial algorithms like the Genetic algorithm help quantitative analysts create trading strategy optimization applications that identify patterns, opportunities, and limitations in existing strategies.

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Interest and Exchange Rate Analysis

Quantitative financial analysts rely on data-based interest and exchange rate metrics to make accurate assessments and value propositions for their clients. But parsing high volume data to get actionable insights requires robust financial analysis tools.

IMSL algorithms help developers quickly add interest and exchange rate analysis functionality to financial applications so financial analysts can make accurate and data-driven decisions.

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Fixed Income Analysis

In fixed income analysis, analysts determine whether to buy, sell, hold, hedge or stay out of securities based on analysis of their interest rate risk, credit risk and likely price behavior in hedging portfolios.

Using the linear and non-linear optimization functions in IMSL Libraries can help drive fixed income analysis functionality.

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Options and Derivatives Pricing

Because many significant problems in financial modeling can be expressed as particular choices of coefficients, initial conditions, and boundary values. The IMSL C library includes a function for solving a generalized version of the Feynman-Kac partial differential equation, allowing applications to efficiently calculate price options on stocks via quantitative financial equations like the Black-Scholes equation.

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Major U.S. Bank Builds Sophisticated Analytical Models With IMSL

For large financial institutions, IMSL algorithms can add immediate functionality and value while significantly decreasing costs on code development and maintenance.

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Why Use IMSL Financial Quantitative Analysis Algorithms?

The IMSL libraries provide users with the software and technical expertise needed to develop and execute scalable, high-performance numerical financial quantitative analysis applications. IMSL libraries save development time by providing pre-written mathematical and statistical algorithms that can be embedded into C, Java, Fortran, and Python applications.

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Get a High ROI With IMSL

With embeddable algorithms in C, Fortran, Java, and Python, developers can seamlessly integrate IMSL algorithms in a fraction of the time it would take to develop algorithms from scratch.

Seamless Production Pipeline

Because IMSL has libraries in C, Fortran, Java and Python, developers can write a prototype in Python or Fortran, then use the same algorithms for production in C or Java without wrapping.

Tried and True Financial Algorithms

Trusted by customers for over five decades, the IMSL libraries are reliable, accurate, and proven to deliver value on numerical applications across all industries.

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Want to see how IMSL Financial Algorithms can work with your application? Request an evaluation today!

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