Risksvr™ is designed to give you all the analytical power you will ever need in the shortest timeframe possible in order to compute absolute or relative market, credit and liquidity risks over one or multiple simulation horizons.

Risksvr™ gives you the modeling power to optimize returns while minimizing exposures against multiple risk factors. Your portfolio's risk characteristics can be sliced and diced according to your own tag hierarchy. 
Each tag(s) can then be used to accumulate quantiles and compute the distribution of returns of the position associated with the tag's. A series of limits, with tolerance levels and event triggers can then be assigned to each tag according to your own formula(s). 
Tag hierarchies can be combined to other tags hierarchies in order to compute relative risks of one series of positions against another set of positions. (i.e. benchmark against portfolios to compute ex-ante tracking error, hedge effectiveness, etc).  

Risksvr provides complete control over Market and credit Data fed into the engine. You can thus measure sensitivities of each Market or Credit Risk Factor with regards to any other piece of information that is used in the computation of Risks. (Positions weight change, recovery rates, edf/ hazard rates, Implied Forward Volatility Interest Rate Spread Sensitivity, interest rate correlation vs equity correlations in equity quantos, covered parity sensitivity in fx-generations, zero coupon vs par rate sensitivity, etc.. 

By running Risksvr™, you can assess how much Market, Credit and Liquidity risk you can sustain before these events actually hit your bottom line either as a balance, a collateral value, a series of limits, a percentage or even a rating! 

In today's volatile marketplace Risksvr™ Financial Risk Management Calculation Server can easily make the difference between consistent returns and unsustainable losses!

Market Risk

Risksvr™ covers the four Market Risk industry standards:

• Historical
• Monte-Carlo
• Parametric or improved Riskmetrics(tm)
• Stress Testing or What-If Analysis

1. In the Historical simulation you forecast events by using the price history to observe what would happen if past events were applied to today's or tomorrow's prices.
2. In the Monte Carlo simulation you forecast events by generating stochastic movements from random paths.
3. In the Parametric simulation you use price sensitivities of your positions to observe assumed worst-case movements.
4. In the Stress Test simulation you measure the impact changes will have on your positions and portfolio. These might be changes completely unrelated to your own holdings. If this is the case Risksvr™ will use the structural properties between assets to condition extreme events into  correlations.

These four methodologies rely on the evolution of all related market risk factors: Individual Interest Rates Vertices within and across base Term Structures, Spread curves, Foreign Exchange Rates, Equities, Commodities, Forward Rates and Option Implied Volatilities Surfaces.

Risksvr™ accumulates results directly in a multinomial bushy tree that contains bucketed hash-map leafs. Each leaf is thus defined as a dimension thanks to a tag that is either associated to a trade, a position, a counterparty a benchmark or a rule. With this, each tag can manage its  own probability density function by accumulating quantiles and moments of the distributions of returns. 
This approach is similar to holding an xml tree in memory. This means you can associate any computed value with one or multiple tags and then proceed to "transform" by slicing and dicing absolute and marginal or incremental results into reports that are deemed strategic to your business.
This approach offers limitless possibilities in terms of risk measurements.

Liquidity Risk

To measure Liquidity risks, Risksvr™ incorporates price slippage either as a number of days until the trade will be unwounded or a bid-ask spread that evolves stochastically throughout time: 

To measure Credit risks, Risksvr™ applies a random counterparty default model based on stochastic recovery rates, transition probabilities and /or conditional marginal default probabilities. 

Credit Curves
Risksvrr™ models default probabilities as a set of credit curve. Each Rating Class owns a specific credit curve that evolves over the simulation horizons.
Expected default frequencies are computed from the marginal default probabilities or Hazard rates extracted from the credit curve.
The credit curve can be built from:

• Expected default probabilities

• Marginal default probabilities or Hazard rates.

• Survival Probabilities.

• One or Multiple Transition matrices. If one single matrix is provided then Risksvrr™ will use the first year statistic provided and apply the transition matrix to the other horizons on the curve. If multiple Transition Matrices are provided, then each one will be used to cover it's respective horizon.
  
  Risksvr™ extends beyond the fixed 1 year horizon and provides a model that can incorporate the full term structure of default probabilities, default correlations, hazard rates and / or transition matrices.

Correlated and uncorrelated default mode methodology:

Risksvr™ incorporates tow different credit methodologies:

1. Correlated Migration: As with standard obligor correlation, Risksvr expects a weighting scheme for each risk factor associated to a counterparty.
   This weighting is usually assumed to follow a country index approach, but any weighting scheme can apply. The weighting is then incorporated into the simulation and mapped to the Normal distribution's cumulative density. In its simplest form, this model falls back to the CreditMetric(tm) model. In it fully fledged form, this model allows for migration between simulation horizons.
   This model expected a rating for each type of spread curve defined in order to compute the full impact of upgrades and downgrades as they take place over the simulation horizons. This model is ideally suited to compute credit derivatives. .
   

2. Uniform Default: If no weightings are provided, Risksvr falls back to it's default model, which applies no correlation between counterparty defaults. Instead Risksvr computes the likely-hood of default from a random uniform draw. If default is observed then recovery is simulated stochastically and then applied to the amount of the loss given default.

Risksvr^(TM) offers the mechanisms to measure bilateral and multi-lateral netting across account  hierarchies. Every counterparty can hold nettable and non-nettable accounts with their own specific collateral and currency. Additionally, you can switch between full nettings, no netting and netting rules and therefore analyze the impact of agreement breakdown.

Risksvr^(TM) can simulate country risk (Sovereign political risk, Inflationary pressure, Devaluation policies, etc) by computing Country Exposures and Country losses.  Country Exposure and Country losses take into account the accounts domiciled in a specific country. Each country can be assigned a rating state, a growth rate and a loss given default in the case that the country hits a default state. 

Risksvr^(TM) can measure funding gaps and long term profitability scenarios of positions. 
Each positions can associate cash-accounts to individual exposures (i.e. legs). Each exposure   can thus credit/debit the cash-account according to the proceeds or costs associated with the positions (coupons, dividends (if paid in cash), payments, option exercise, etc. 
Cash-accounts can thus mimic very closely what happens in practice. 
You can therefore take into account movements of collateral posted to fund these positions as well as estimate the impact of reinvestment policies.

^Risksvr(Tm) is designed as a generic reentrant modular engine.

Each component is connected to the other via generic streams. 
Reports can thus be produced for every piece of information processed by ^Risksvr(Tm).

Better yet, every process can be overridden by user defined input data, This means you have complete control over each calculation step  in the engine., which greatly facilitates replication, validation and benchmarking.

For example, you can decide to override the internal engine and inject your own Random data, your own forward rates, your own Zero Coupon Rates, your own  Factored Matrix, your own unitized returns, your own  Pricing Routines, your own Marginal / Conditional Default Probabilities, your own Counterparty factorized weights and mappings, etc. You can also switch between unlimited sources of market data (file, sockets, db)  (see Template Data / Benchmarking)  which greatly facilitates checking of results.

Risksvr^(TM) accumulates results according to the tags you have associated with your positions. In Risksvr^(TM) jargon, a tag is a URL like keyword that is defined with one or a group of trades. Tags support the same syntax as conventional URLs. (e.g.: /portfolio/fx/USD could be a valid tag).

By design and for efficiency, Risksvr^(TM) accumulates marginal sensitivities of the trade's contribution with regards to other holdings that form a position and portfolio. This means you can request the risk sensitivity of the percentage weight of each individual trade with regards to Absolute or Relative VaR. This has important consequences in terms of hedge analysis, real-time value-at-risk and portfolio diversification and concentration effects.

Risksvr^(TM) comes with a number of pre-defined industry standard settings in order to estimate fair values and price instruments. These default settings can always be overridden if such a need arises.

The engine is based upon generic financial concepts that can be relaxed or constrained in order to create entire sets of assumptions that steer the risk model.

The reason for this lies in user friendliness and the inherent complexity of risk management system.
Indeed, in most cases if you don't ask, then you probably don't need to know.

This obviously makes your life a lot easier, since you don't need to deal with complex setup values which only Risk Management professionals are familiar with. On the other hand you can always change these values if want to tweak results according to your own set of assumptions.

Detailed Information is Available in the Online Documentation of the Risksvr™ engine

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