Open Source Risk Engine (ORE)

Open Source Risk Engine (ORE) is a transparent, peer-reviewed framework for pricing and risk analysis. It is built upon QuantLib and extends it with additional simulation models, financial instruments, and pricing engines.

Key capabilities include:

• Contemporary risk analytics and value adjustments (XVAs).
• Interfaces for trade/market data and system configuration via API and XML.
• Application launchers for Excel, LibreOffice, Python, and Jupyter.
• Comprehensive test suites and various use-case examples.

Example 65 serves as a demonstration of the following engine capabilities:

• Portfolio: Contains a Flexi Swap in EUR.
• Market: Uses a pseudo market data set (dated 5/02/2016).
• Pricing: Implements multi-curve pricing, utilizing separate discount and forward curves.
• Analytics: Performs sensitivity analysis, including par sensitivities.

Example 46 demonstrates the pricing and analytics of a Total Return Swap on a CashFlow CDO (Collateralized Bond Obligation), modeled as a CBO, in USD.

Key components of this example include:

• Portfolio: A reference portfolio consisting of 10 pseudo bonds.
• Market Data: Pseudo market data dated 2020-12-31.
• Pricing Engine: Uses QuantExt::MonteCarloCBOEngine with 10,000 Monte Carlo samples.
• Analytics: Calculates the Net Present Value (NPV).

This example demonstrates how to use the scripted trade representation within the engine. It covers the following components:

1. Portfolio

The example includes several complex instrument types:

• Vanilla Equity Option
• Equity Barrier Option
• Equity Accumulator

2. Market

Uses market data snapshots (e.g., as of 5 June 2023) to drive pricing.

3. Pricing

Demonstrates multiple pricing methodologies:

• Analytic pricing: Used for reference.
• Monte Carlo pricing: For scripted payoffs.
• Finite Difference (FD) pricing: For scripted payoffs.

4. Analytics

Calculates key risk and value metrics:

• NPV (Net Present Value)
• Cashflows

Legacy Example 11 demonstrates a complete risk engine workflow involving the following components:

1. Portfolio Configuration

• Instrument: 20Y EUR swap.
• Notional: 1M.
• Cashflows: Receive 3% semi-annually and pay EURIBOR 6M semi-annually.
• Netting Set: CPTY_A.

2. Market Data Setup

• Yield Curves: Curves dated 20/05/2016.
• Volatility Surfaces: Normal cap volatility surfaces for EUR, GBP, and USD.

3. Pricing Methodology

• Model: Multi-curve pricing (EUR collateralised).
• Cap Pricing: Normal (Bachelier) model.

4. Analytics Produced

• The example compares several risk metrics:
  • EPE (Expected Positive Exposure)
  • EE_B
  • EEPE_BB
  • EEE_BB

Example 7 demonstrates the following financial modeling capabilities:

• Portfolio: Modeling an FX Forward (USD/EUR, 10y maturity) and FX EUR/USD Call and Put Options (10y expiry).
• Market Data: Uses a pseudo market state as of 2016-02-05.
• Pricing Engine: Implements cross-currency discounting using EUR collateral.
• Analytics: Calculates FX Forward Expected Positive Exposure (EPE) and Expected Negative Exposure (ENE), compares them to FX Put/Call Options, and calculates FX Option EPE.

This example demonstrates the integration of credit and market risk within ORE. It utilizes a pseudo market (dated 5/02/2016) and implements multi-curve pricing with separate discount and forward curves.

Key features demonstrated include:

• Portfolio Composition: Supports test portfolios ranging from a single bond or CDS to complex sets of 100 bonds, including mixed bond/swap and bond/CDS portfolios.
• Pricing: Uses multi-curve methodology (separate discount and forward curves).
• Analytics: Calculates Credit Migration Risk.

Legacy Example 23 demonstrates the following engine capabilities:

• Portfolio Management: Handles Forward Rate Agreements (FRA) and Averaging Overnight Swaps.
• Market Data: Utilizes pseudo market data (dated 2016-02-05).
• Pricing Models: Implements dual curve pricing using Eonia Discounting and Euribor Forwards.
• Analytics: Calculates Expected Positive Exposure (EPE) and Expected Negative Exposure (ENE), and compares them against European payer and receiver swaption prices.

The Scripted Trade framework allows for the representation and pricing of complex financial instruments. This framework provides two ways to represent trades:

1. Classic ORE XML: Trades are represented using standard ORE XML schemas and priced using QuantLib-style methods. This is used for standard products like European and American Equity Options.
2. Scripted Trades: A more flexible representation used for all product types, including complex instruments like Equity Barrier Options and Equity Accumulators.

For a complete technical specification of how to implement these, refer to the ORE Scripted Trade documentation and the ORE Product Catalogue.

Legacy Example 2 demonstrates a complete risk engine lifecycle involving the following stages:

1. Portfolio Setup: Defines a Vanilla Swap in EUR with a 10k notional and 20Y maturity. The swap is at-the-money (ATM) with a recommended fixed rate of 0.99851% and pays 6m Euribor.
2. Market Data: Uses pseudo market data dated 2016-02-05.
3. Pricing Engine: Employs a dual curve approach using Eonia Discounting and Euribor Forwards.
4. Analytics: Calculates Expected Positive Exposure (EPE) and Expected Negative Exposure (ENE), comparing these results against European payer and receiver swaption prices.

The Example 6 demonstration utilizes the following engine capabilities:

Market Data

• Uses yield curves (dated 20/05/2016).
• Uses normal cap volatility surfaces for EUR, GBP, and USD.

Pricing Engine

• Implements Multi-curve pricing (specifically EUR collateralised).
• Uses normal (Bachelier) cap pricing models.

Analytics

• Calculates and compares EPE (Expected Positive Exposure) and ENE (Expected Negative Exposure) for various trade combinations and netting sets.