The Quant Innovation Mesh 1.1
For UnRisk Capital ManagerThe Type - Time Type, Reality Type, Style Type, Structure Type, Matter Type
Realistic, Real-Time, Documentary, MultiFlow, Financial Risk Management, Market Risk, Valuation and Data Management FactoryPurpose - What's the innovation for
Risk Thriller (Expose Risky Horrors and manage them)External Value at Use
Market opportunity creation by risk informed decisionsInternal Type
Empower small capital management firms.(for better portfolio engineering and investment management)Internal Value at Use
Fundamental quant work and automated risk services in one…represented as know-how packagesPoint of View - what is the characteristic domain, scope and generality
Instrument risk, portfolio riskObjects of Desire
Conserve capital, set the stage for successful risk management, leverage technology, build quant finance skillsObligatory tasks and conventions
Manage risk in a regulation and business framework 1. Model validation and robust model calibration 2. Portfolio across market behavior simulation, VaR calculations, Tail risk analysis…4. Portfolio re-engineering and -construction…5. Contribution to enterprise risk managementRealization - How the innovation works and what it needs
The controlling idea - approaches to simulate, control and risk manage complex systems
Build and profile the foundation, value deal types correctly, build smart portfolios, value nested portfolios across nested scenarios, calculate characteristic risk spectra, document them, help to transform them into insight for decisionsHook.Build.Payoff - How it transforms objects, controls processes and supports decisions
Preprocessing - validate the foundation
Set-up users, instruments, models and market date, valuation regimes...Problems
Model, method and calibration risk (technological risk) may add toxicity to instruments (and even deal types)Crisis
Operational risk becomes horrible in interplaySolution
Change valuation regimes as result of model-method scenarios-----------------------------------
Processing - manage portfolio risk
Set-up portfolios, scenarios, simulations…simulation regimesProblems
Select and apply the right risk analytics methodologies, meet the massive valuation and data management requirements...Crisis
A portfolio shows unexpected riskiness with respect to certain market behavior, but it's difficult to quantify the influence of instruments, risk factors, observed periods, correlations…Solution
Comprehensive tests checking such influences…and help re-engineering the portfolio----------------------------------
Postprocessing - aggregate, visualize, report
Set-up a further, more global risk insight and reporting framework, like for cross business units, market segments or territory analytics.Problems
Further analytics need additional data sets and consequently algorithmic treatment.Crisis
Additional data may be uninformative related to the desired objectives and attribute more to noise than dependencies...Solution
Use data driven techniques (statistics, machine learning) or dynamic visualization to curate these data sets with respect to the purpose-------------------------------------
Model risk quantification, "toxic" instrument detection. Portfolio risk dependencies and quantification. Rusk impact on business decisions analytics. Real-tome treatment of massive valuations and data. Full evidence and transparency. Explorative procedures.Conventions - about data and evaluation management, models and methods, programming paradigms, deployment services, performance...
Valuation and data management are twins. It's built multi-model and multi-method. It's a high performance system. It comes with a web front-end.Technologies needed
UnRisk CM is built of the UnRisk Technology Stack that needs the Wolfram Technology Stack. UnRisk Financial language is en extension of the Wolfram Language implemented in UnRisk Engines (atop Wolfram gridEngines and proprietary algorithmic technologies)Other quant innovations will have other phases and flows, but if they are of the risky horror type, they will need a kind of bottom up approach for early error, anomaly, danger…detection and management.
The intention of the scheme is providing insight whether the innovation will work in principle. Later on, I'll go through through some details…more on risk, the idea of fragility, why it's sometimes wrong to use probability, the danger of correlation, why simpler models are often better, why calibration does not always work…and why programming needs a new fashion, about parallelism and what platform agnostic means. All from the perspective: will it work and sell.
They will not have a special order, I'm afraid…and I'll inevitably create further The Quant Innovation Mesh releases.
However, if you think I'll be able do support you with your quant innovation making and marketing. I'll be pleased. Contact me here