Post Trade
Post Trade Solutions has launched Risk Analytics Lab, a powerful new platform combining the flexibility of the Open Source Risk Engine (ORE) with the speed and simplicity of SaaS. Built for risk management professionals, the solution provides a smarter, faster and more collaborative approach to managing risk.
Risk Analytics Lab provides a new and innovative way to access complex risk calculations. Users can script their own risk analysis in a familiar Python notebook interface and immediately access key Post Trade Solutions’ (formerly Acadia) data stores, including trade and agreement data. This allows firms to immediately run analytics and generate meaningful results, without waiting for full trade integration. With integrated market data and preconfigured bootstrapping and correlation workflows, users can perform risk calculations instantly - no lengthy setup, and no complex integration projects.
The Open Source Risk Engine (ORE) underpinning the capabilities of Risk Analytics Lab offers complete transparency and configurability. Every line of code is accessible for review, with default configurations that can be extended or adapted as needed. Firms benefit from a robust base configuration that delivers immediate value, while retaining the depth of analytics required to meet complex and custom needs.
Risk Analytics Lab offers a free point of entry to cutting-edge risk and pricing capabilities, while tapping into a growing community of users to enable shared innovation and industry-wide challenges to be tackled together.
Transforming the culture of risk management
Quantitative risk management is one of the most complex areas of finance - demanding deep market knowledge, advanced mathematical expertise, and sophisticated system design. It’s an ecosystem that attracts highly skilled professionals from diverse academic backgrounds, yet these teams often face barriers when pushing for innovation. Demonstrating the value of new solutions can be slow and resource-intensive, making it harder to gain buy-in internally.
By combining the openness and flexibility of open source with the immediacy and scalability of SaaS, Risk Analytics Lab removes the friction that has historically slowed progress. It gives risk teams the tools to innovate faster, collaborate more effectively, and demonstrate value from day one.
Risk Analytics Lab reflects a rapidly evolving space, where a more open and connected approach not only benefits the industry as a whole, but aligns with the mindset of risk professionals already embracing open-source tools as standard. Running on cloud architecture enables users to dynamically scale to meet their requirements.
Key advantages:
- Free to get started – It’s free to get started with Risk Analytics Lab, offering risk managers the option to eliminate or significantly reduce costs, making it a more viable investment for firms.
- SaaS solution – Risk Analytics Lab can be launched immediately, enabling teams to illustrate its capabilities out of the box, while retaining the ability to scale to meet the needs of the hardest risk calculations
- Open Source – Risk Analytics Lab is open source, allowing users to customise and collaborate, fostering a more community-driven approach to pricing and risk analysis.
- Integrated market data – Risk Analytics Lab enables firms to access and customise prepopulated data sets such as trade and agreement data, empowering teams to price complex products, generate sensitivities, conduct xVA simulations, validate models, and much more.
Functionality and use cases of Risk Analytics Lab
Pricing complex products:
- Advanced mathematical models – Using stochastic processes (e.g., Black-Scholes, Hull-White) and standard algorithms (finite difference methods, American Monte Carlo) for accurate pricing of exotic derivatives.
- American Monte Carlo & Lattice Methods – Implementing American Monte Carlo simulations and Finite Differences for path-dependent products like barrier options, auto-callables, and Asian options.
- Fast & Scalable Computation – Leveraging parallel computing and cloud-distributed processing to enable large loads to be easily processed.
- Market Data Integration & Calibration – Integrated with the Post Trade Solutions market data service, providing clean market data for pricing and calibration of models.
- Sensitivity Analysis with AAD – Models have integrated AAD capabilities, enabling fast calculation of trade-level sensitivities for complex products.
xVA simulation:
- Monte Carlo Simulation & Stochastic Modelling – Implementing Monte Carlo methods to estimate Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), and other xVA components.
- Efficient Exposure Calculation – Using path-dependent modelling to compute Expected Exposure (EE) and Potential Future Exposure (PFE), while optimising computational performance.
- Support for GPU parallelisation for further performance optimisation.
- Integration with Market Data – Leveraging the Post Trade Solutions market data capabilities allows firms to benefit from cleansed data for risk-neutral calibration and derivation of correlation matrices.
- Regulatory Compliance & Reporting – Suitable as a basis for Basel III and IFRS 13 reporting.
- Flexible and Scalable Framework – Exposure through Python allows users to extend to their own needs and integrate with further downstream systems.
Model validation:
- Open-Source Model Library – The Open Source Risk Engine library of models can be an ideal choice as a challenger engine, providing transparent, open-source implementations of valuation and simulation models.
- Integrated with Market Data – Leveraging the Post Trade Solutions market data library and history to deliver clean market data for pricing and historical data for model calibration.
- Automated Model Backtesting – Implementing automated statistical analysis on historical data for market and credit risk to ensure that models perform well initially and maintain performance over time.
- Regulatory Compliance & Governance – Supporting regulatory standard calculations (e.g., CCAR, SA-CCR, FRTB-SA, CVA-SA) for benchmarking purposes.
- Sensitivity & Stress Testing – Assessing model stability under extreme conditions using stress testing scenarios and sensitivity analysis (e.g., perturbation testing).
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