Custom Developed Derivatives Pricing and Calibration Models
SciComp Consulting provides ready-to-use, efficient derivatives pricing and calibration products that can be precisely tailored to customer specifications.
Derivatives Pricing Models
Unlike vendors that must rely on inflexible pre-built libraries, SciComp’s proprietary technology allows optimal numerical techniques and methods to be chosen for each model, reasonably and with rapid turn-around.
Features such as industry-standard or proprietary underlying model dynamics, financial contract provisions, and calibration routines can be combined to produce customer specified pricing models for any asset class. Pricing and calibration routines can be integrated with existing systems or used as standalone products.
In addition, SciComp Consulting can provide significant performance acceleration for both PDE and Monte Carlo based pricing models with an OpenMP-compliant (muti-core CPU processing) or CUDA-enabled (NVIDIA GPU) version of the model.
All asset class support
SciComp Consulting develops derivatives pricing models across all asset classes, including but certainly not limited to:
- Equity Derivatives
- FX Derivatives
- Commodity Derivatives
- Convertible Bonds, Universal CB Pricing Model
- Primary and Secondary Bonds
- Interest Rate Derivatives
- Cross Currency Structures
- Energy Derivatives
- Credit Derivatives, STCDO, CDS
- Hybrid Instruments
Underlying model dynamics
Industry standard underlying dynamics include, but are not limited to:
- Local volatility models (LV)
- Stochastic volatility models (SV), including asset (SVJ) and variance jumps (SVJJ)
- Stochastic local volatility models (SLV)
- SABR, Levy models, including stochastic time change, VG, CGMY, CGMYSA, etc.
- Schwartz, Gabillon, with Stochastic Vol extensions
- Log-normal forward models with local volatility
- Single and multi-factor short rate models
- Libor Market Models (LMM), Lognormal, local or stochastic volatility, SABR
- Credit Models
- Reduced form approaches
- Structural/Firm value approaches
- Multi factor models
- Implicit joint dependence
- Copulae in Monte Carlo
- Semi-analytic method of Andersen, Sidenius and Basu
- Large Pool Base Correlation
- Stochastic recove sry models (MC and semi-analytic)
Arbitrary user defined models: SciComp Consulting supports the implementation of any derivatives pricing model valued using systems of partial differential equations (PDEs), stochastic differential equations (SDEs), or analytic functions. Therefore users may define a nearly unlimited range of public-domain and proprietary models.
Model integration features
SciComp Consulting products do not impose proprietary data models, so code integration does not require wasteful data containers or format conversions. All pricing and calibration products are available as Excel spreadsheet/add-ins, Windows/Unix executables (can be embedded in Java, Python, COM, or .NET wrappers) or C++ source code.
Custom Calibration Routines
SciComp Consulting can quickly and economically implement custom calibrators for users looking for either public domain or proprietary calibration routines that are tailored to their specific needs and requirements. Custom Calibrators are available for a broad range of pricing models including:
- Equity/FX/commodity models including many parameterized local volatility, stochastic volatility (with and without jumps), and pure jump models.
- Generic short rate models (including popular Gaussian and lognormal flavors) with interest rate and/or hazard rate calibration to volatility term structure of cap/swaptions and CDS spreads or corporate bonds.
- LIBOR market model calibration. Several parameterizations of the correlation matrix are available as well as several approaches for including volatility skew.
- Credit models, including survival/hazard rates from credit spreads, base correlation and semi-analytic models with deterministic or stochastic factor loading.
Many models accommodate can time dependent parameters, either exactly through numerical models of the calibration instruments, or through very fast approximate analytic techniques. Several robust optimization techniques are available, both gradient-based and stochastic.
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