An advanced portfolio simulator to help you design
investment programs that can withstand various market scenarios.

Simulation Steps:

1) Map each security to one or more risk invariants, such as: rates, currencies, credit spreads and underlying.

2) Build a covariance matrix using modern statistical techniques, preserving only strong causality.

3) Generate Monte Carlo simulations for normal/log-normal risk invariants.

4) Fully reprice each security in a near horizon and compute profit and loss distribution.

5) Extract security-level risk properties from profit and loss distributions, such as Conditional Value at Risk (CVaR).

Risk Measures:

Everysk computes dozens of risk measures per security (bottom-up) or per factor (top-down). The main simulated ones are:

• Marginal Contribution to Total Risk (MCTR): measures the contribution to risk from the security or factor. Risk can be volatility or Value-at-Risk (VaR).
• Conditional Value-at-Risk (CVaR-): measures the expected profit and loss for the worst simulated outcomes. Represents a "worst case" scenario. Also known as Expected Shortfall.
• Conditional Value-at-Risk (CVaR+): measures the expected profit and loss for the best simulated outcomes. Represents a "best case" scenario.
• Expected Value (EV): measures the expected profit and loss for the full distribution of simulated outcomes. Represents a "base case" scenario.

Additionally, our Risk Engine can compute dozens of security Greeks and returns-based metrics:

Greeks:

Delta, Vega, Gamma, Theta, Convexity, Notional Duration, CS01, Risk Weighted Implied Rating, Risk Weighted Implied Duration and others.

Returns-Based:

Maximum Drawdown, Positive and Negative Streaks, Standard and Downside Deviations, Sharpe and Sortino Ratios, Alpha, Beta, Correlation, R2 and others.

Transitivity in Stress Testing:

Exogenous market shocks tend to propagate and impact all securities in your portfolio.

Most systems are designed to calculate the impact in a localized manner: interest rate scenarios affect only fixed income, equity scenarios affect only equities.

Everysk calculates full propagation, allowing money managers to probe scenarios in a more reliable fashion and avoid losses.

Data Structure:

• Efficient data structures to perform bottom-up risk aggregation and top-down factor modeling.
• Risks at the security level can be aggregated into GICS sectors, security types, countries-of-risk, liquidity, market capitalization, customized labels, implied rating, duration buckets and dividend yield buckets.
• Both security and factor risk concentrations add up to total portfolio risk, making it easier to understand each component.

Financial Data:

• Everysk is integrated with many brokers and administrators. Integrations have smart logic to reconcile positions from multiple sources.
• Positions, margin and cash are automatically mapped and normalized into tagged and dated portfolios.
• Market, company, reference and alternative data are converted into high-value, actionable portfolio analytics.

Customizable:

• Flexible symbology allowing for custom bonds, futures or options to be created for further analysis.
• Design your own factor model on the fly. Everysk will automatically compute the explanatory power of your factors and the amount of residual risk.
• Efficiently upload your own alternative datasets using Everysk's custom integrations.