Struct RiskMetrics 

pub struct RiskMetrics {
    pub volatility: Decimal,
    pub value_at_risk: Decimal,
    pub expected_shortfall: Decimal,
    pub coefficient_of_variation: Decimal,
    pub sharpe_ratio: Decimal,
}

Represents a collection of key financial risk metrics used in risk analysis and performance evaluation.

Overview

The RiskMetrics structure encapsulates a set of quantitative measures that assess the risk and performance characteristics of a financial portfolio, asset, or investment strategy. These metrics provide insights into the variability, potential losses, and overall return of the analyzed entity.

Applications

The RiskMetrics structure is commonly utilized in:

• Portfolio Management: To assess and balance risk-return trade-offs
• Risk Management: To estimate potential losses and evaluate market exposures
• Performance Analysis: To compare against benchmarks and alternative strategies
• Financial Modeling: To perform stress testing and scenario analysis

Related Concepts

• Metrics: Aggregates risk metrics alongside other statistical measures
• Financial Risk Indicators: Additional measures like maximum drawdown, Treynor ratio, and Sortino ratio

Note

For accuracy, ensure the data used to compute these metrics represents a sufficiently long time horizon and is free from anomalies.

Fields

volatility: Decimal

Measures the degree of variation in returns over time. Represents the standard deviation of returns and is a widely used indicator of market risk.

value_at_risk: Decimal

Quantifies the maximum expected loss over a defined time horizon with a given confidence level (typically 95% or 99%). Essential for understanding tail-risk and evaluating potential losses.

expected_shortfall: Decimal

Represents the average loss in worst-case scenarios beyond the VaR threshold. Provides a more comprehensive assessment of tail risk than VaR alone.

coefficient_of_variation: Decimal

The coefficient of variation, std_dev / mean: the population standard deviation of the values (sqrt(sum((x - mean)^2) / n), the std_dev of BasicMetrics) over their mean, signed as the mean. It measures dispersion relative to level and is unitless. Curves and surfaces compute it the same way; it is undefined at a zero mean, which the computation reports as MetricsError::ZeroMean, and zero for empty data, like every field here. It replaces beta, which promised a sensitivity to market returns that no curve or surface has the data for (#824).

sharpe_ratio: Decimal

Indicates the risk-adjusted return by comparing the portfolio’s excess return (return above the risk-free rate) to its volatility. Helps evaluate whether returns justify the associated risk profile.

Trait Implementations

impl Clone for RiskMetrics

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl ComposeSchema for RiskMetrics

fn compose(generics: Vec<RefOr<Schema>>) -> RefOr<Schema>

impl Copy for RiskMetrics

impl Debug for RiskMetrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Display for RiskMetrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Serialize for RiskMetrics

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl ToSchema for RiskMetrics

fn name() -> Cow<'static, str>

Return name of the schema.

fn schemas(schemas: &mut Vec<(String, RefOr<Schema>)>)

Implement reference utoipa::openapi::schema::Schemas for this type.