· Future Release

· Version 13 – New Features (May 2025)

· Version 12 – New Features (October 2024)

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Below is the foreseeable roadmap of further Acadia contributions to ORE in the second half of 2025 and in 2026:

Regulatory capital

• Market Risk Capital (FRTB-SA)
• Credit Risk Capital (SA-CCR, BA-CVA, SA-CVA) for all products
• SA-CCR refactoring, aligned to ISDA’s Capital CRIF

Performance

• Development of XVA Sensitivity Analysis using AAD continues, which is demonstrated in proof-of-concept stage in ORE example 56
• The GPU interface implementation will be extended to cover exposure simulations
• A GPU interface implementation in CUDA will follow with benchmarking examples vs the OpenCL implementation that is available in release 12

Pricing & Simulation

• The Heston model will be exposed in ORE for pricing of equity derivatives
• The Multi-factor Cross Asset Model based on n-factor Hull-White will be extended beyond its current coverage of IR, FX, COM asset classes, also adding calibration procedures
• Stochastic volatility
• Hardened Dynamic SIMM
• Quadratic Gaussian model
• Multi-factor commodity model
• Global rate curve building (global for the instruments in one curve and for several curves that are mutually dependent
• Stressed Cashflows/CfaR

Instruments

• Callable and puttable bonds support (including bond derivatives)

User Interfaces & Tools

• Extension of the open-source-risk-engine scope to cover more of ORE’s classes and member functions. This will be done tactically as we see concrete demand from clients

See some examples here (https://github.com/OpenSourceRisk/ORE-SWIG/tree/master/OREAnalytics-SWIG/Python/Examples)

• exploRE (AI documentation assistant)
• ORE Dashboard/GUI
• OREDiscovery (market data dependency tool and configuration building)
• Creation of an ORE interface in Excel

ORE Academy

The following 3 topics are currently being produced, and these videos will be released in the coming months. They will all come with an Excel spreadsheet replicating what is happening in our C++ library, while also explaining how to configure the relevant XML files:

• Equity Option Valuation & Calibration:
  • Part 4 – Equity Volatility Calibration
  • Part 5 – Root-Finding Algorithm Explanation
• Sensitivity Calculation
• Parametric VaR

We will also release some tutorials showing how to use our ORE Python library in some Jupiter Notebooks, with similar topics:

• ORE Python – Equity Option Valuation
• ORE Python – OIS Discounting Curve Bootstrapping

An update of the installation video in Windows is also planned, along with one on schedule creation.

See the YouTube channel here (https://www.youtube.com/@oreacademy)

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Examples

80+ use cases, reorganized into:

• Products and Scripted Trade
• Exposure with and w/o Collateral
• Initial Margin
• Market Risk
• Credit Risk
• XVA Risk
• American Monte Carlo and Performance
• ORE-Python and ORE-API

Documentation

1000+ pages, now split into three main PDF documents

• User guide
• Product Catalogue
• Methods

Additionally, you can find:

• Scripted Trade
• Credit Model
• Design

ORE Python

Code base moved into the ORE repository, including Jupyter notebook examples

Consolidation with QuantLib

ORE v13 uses QuantLib v1.38, released end of April 2025

Instruments & Pricing Engines

• Swaption extensions (mid-coupon exercise)
• Convertible Bond extensions (triggers for soft call rights)

Pricing & Simulation

• SABR model calibration
• FX vol surface improvements (stabilized calibration, weighted interpolation)

ORE Python

Extensions to facilitate ORE integration at a major client to replace a production PFE system

Analytics

• Stressed Cashflows
• Decorrelated Backtesting
• Cross Asset Model Calibration Export/Import
• Portfolio Details
• SA-CCR, BA-CVA, SA-CVA for some products
• IR/FX CRIF Generation for Dynamic SIMM

Performance

• Cache large in-memory reports on disk
• Reduce memory footprint of exposure cubes
• Speed up the Cross Asset Model analytics
• Improve the American Monte Carlo Framework
  • Add equity
  • Serialize paths and regression models
  • Recalibrate on closeout dates
  • Optional recalibration under scenarios to stabilize risk results
  • Expose indicator smoothing parameters

Dynamic SIMM Model – POC

• Systematic deviations to be ironed out, see below
• Sensitivity regression is more sensitive than NPV regression
  • IM distributions close to today seem to need regularized parametric regression
  • IM distribution close to option expiry need local regression to match the true distribution
• Optimal regression choice to be determined

ORE Academy

Release of the ‘Equity Option Valuation & Calibration Masterclass’ series:

• Part 1 – Fundamentals & Risk Measures
• Part 2 – Trade Configuration
• Part 3 – Equity Forward Curve Construction

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The rollout of formerly proprietary code (Post Trade Solutions (formerly Acadia)’s ORE+) continues, but new development has overtaken the latter, notably regarding the extension of instruments, pricing engines, analytics, performance, integration and the ORE Academy.

Instruments & Pricing Engines

• Formula-based leg (see Example 64)
• Callable Swap (see extended Example 5)
• Flexi Swap and Balance Guaranteed Swap (see Examples 65 and 66)
• Outperformance Option, Pairwise Variance Swap
• American Swaption with finite difference pricing in LGM (see extended Example 4)
• Finite difference LGM pricers for Bermudan and European Swaptions
• Scripted Trade pricing using LGM with finite difference and numerical integration
• Improved AMC regression model for Resetting Cross Currency Swaps
• Yield curve building with mixed interpolations (see Example 53)
• SABR pricing for Swaptions and Caps/Floor (see Example 59)
• Analytic pricer for Overnight Index Swaptions
• Numerical integration LGM pricer for European Swaptions
• Burley 2020 scrambled Sobol sequence for faster convergence (see Example 56)
• Separate notional payment lags for Resetting Cross Currency Swaps
• Support for indexed cashflows in American Monte Carlo
• Rules-based Bermudan exercise dates

Analytics

• IM Schedule analytic (see extended Example 44)
• Scenario analytic (see Example 57)
• Bond spread imply
• Support overlapping close-out date grid in exposure/XVA (see Example 60)
• Market risk backtest analytic, calculation of traffic-light bounds
• Historical simulation VaR analytic (see Example 58)
• P&L and P&L Explain analytics (see Example 62)
• Stress test in the par rate domain (see Example 63)
• XVA Stress testing (see Example 67)

SIMM

• SIMM 2.7 Calculation from CRIF Files

Performance

The Scripted Trade framework serves as ORE’s Adjoint Automatic Differentiation (AAD) backbone and interface to external compute devices (GPUs):

• AAD is used case by case, where appropriate, to accelerate sensitivity analysis in Post Trade Solutions (formerly Acadia) Risk Services
• Near term goal is utilizing GPUs to enhance the performance of backtesting, sensitivity analysis (CRIF generation) and exposure simulation in the Post Trade Solutions (formerly Acadia) Risk Services

Related new examples:

• XVA Sensitivity using AAD, see Example 56
• Fast Sensitivities using AAD and GPUs, see Example 61

Integration

In the Post Trade Solutions (formerly Acadia) Risk Services we run ORE wrapped into a Web Service framework, deployed via Docker on multiple nodes for industrial scale (“RESTORE”, still part of Post Trade Solutions (formerly Acadia)’s proprietary ORE+)

In release 12 we have contributed a similar – proof-of-concept – web service version of ORE based on the ORE Python module and the Flask web framework.

Related new example:

• Examples/API, see user guide section 5.0

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