Solstice is an economic network simulation framework
A brief overall description of the economic network simulation engine Solstice (NB: much more in the manuals / docs).
Solstice is an economic network simulator. The primary outcomes are quantitative analyses of the behavior of economic systems under uncertainty. It can be used both as a production tool in a portfolio / risk management context or as a research tool.
The objective is to provide a performant, easily usable, extensible simulation framework to support economic network analysis.
- Set of c++ library objects / methods implement the Solstice framework
- Assorted auxiliary code / scripts
- Documentation
- Illustrative implementation of toy problems
- Sample data sets
- c++17
- cmake
- conan
- poco++
- eigen
- statslib (including gcem dependency)
- catch2
Installing these dependencies is system dependent, please follow instructions as per your situation. (In the future we will have a Docker based installation that can simplify this process)
- Solstice is written in C++17
- The framework is "network ready". I/O can be file based or over http.
- It uses Poco++ for many of the common app functionalities
- It uses Eigen as the core container of numerical data (vectors / tensors) and linear algebra algorithms
Solstice adopts in its implementation a number of features of recent entity-component-system C++ frameworks. This favors composition over inheritance in certain critical objects. Runtime polymorphism allows the flexible construction and extension of Solstice to enable the analysis of a variety new models and network structures
An indicative list of econometric models and associated financial concepts implemented
- Multiperiod - Macro Scenario Generator (VAR type)
- Single factor
- Equity type multi-factor
- Macro-economic multi-factor
- Single Period - Markov Scenario Generator (Graph type)
- Conditional independence
- Contagion / network models
- Collateral Value Simulation
- Regulatory Capital Calculation
- monte carlo - simple
- monte carlo - with importance sampling
- asymptotic limit (large N)
- analytic functions
- moments / analytic approximations
- regulatory capital (ASFR)
- rating distributions at different timepoints
- quantile loss result at [99.XX] / other distribution statistics
- results statistical errors / confidence levels
- expectations at future timepoints
- risk capital allocation