Risk Metrics Calculation

Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis.

When to Use This Skill

• Measuring portfolio risk
• Implementing risk limits
• Building risk dashboards
• Calculating risk-adjusted returns
• Setting position sizes
• Regulatory reporting

Core Concepts

1. Risk Metric Categories

2. Time Horizons

Intraday:   Minute/hourly VaR for day traders
Daily:      Standard risk reporting
Weekly:     Rebalancing decisions
Monthly:    Performance attribution
Annual:     Strategic allocation

Implementation

Pattern 1: Core Risk Metrics

import numpy as np
import pandas as pd
from scipy import stats
from typing import Dict, Optional, Tuple

class RiskMetrics:
    """Core risk metric calculations."""

    def __init__(self, returns: pd.Series, rf_rate: float = 0.02):
        """
        Args:
            returns: Series of periodic returns
            rf_rate: Annual risk-free rate
        """
        self.returns = returns
        self.rf_rate = rf_rate
        self.ann_factor = 252  # Trading days per year

    # Volatility Metrics
    def volatility(self, annualized: bool = True) -> float:
        """Standard deviation of returns."""
        vol = self.returns.std()
        if annualized:
            vol *= np.sqrt(self.ann_factor)
        return vol

    def downside_deviation(self, threshold: float = 0, annualized: bool = True) -> float:
        """Standard deviation of returns below threshold."""
        downside = self.returns[self.returns < threshold]
        if len(downside) == 0:
            return 0.0
        dd = downside.std()
        if annualized:
            dd *= np.sqrt(self.ann_factor)
        return dd

    def beta(self, market_returns: pd.Series) -> float:
        """Beta relative to market."""
        aligned = pd.concat([self.returns, market_returns], axis=1).dropna()
        if len(aligned) < 2:
            return np.nan
        cov = np.cov(aligned.iloc[:, 0], aligned.iloc[:, 1])
        return cov[0, 1] / cov[1, 1] if cov[1, 1] != 0 else 0

    # Value at Risk
    def var_historical(self, confidence: float = 0.95) -> float:
        """Historical VaR at confidence level."""
        return -np.percentile(self.returns, (1 - confidence) * 100)

    def var_parametric(self, confidence: float = 0.95) -> float:
        """Parametric VaR assuming normal distribution."""
        z_score = stats.norm.ppf(confidence)
        return self.returns.mean() - z_score * self.returns.std()

    def var_cornish_fisher(self, confidence: float = 0.95) -> float:
        """VaR with Cornish-Fisher expansion for non-normality."""
        z = stats.norm.ppf(confidence)
        s = stats.skew(self.returns)  # Skewness
        k = stats.kurtosis(self.returns)  # Excess kurtosis

        # Cornish-Fisher expansion
        z_cf = (z + (z**2 - 1) * s / 6 +
                (z**3 - 3*z) * k / 24 -
                (2*z**3 - 5*z) * s**2 / 36)

        return -(self.returns.mean() + z_cf * self.returns.std())

    # Conditional VaR (Expected Shortfall)
    def cvar(self, confidence: float = 0.95) -> float:
        """Expected Shortfall / CVaR / Average VaR."""
        var = self.var_historical(confidence)
        return -self.returns[self.returns <= -var].mean()

    # Drawdown Analysis
    def drawdowns(self) -> pd.Series:
        """Calculate drawdown series."""
        cumulative = (1 + self.returns).cumprod()
        running_max = cumulative.cummax()
        return (cumulative - running_max) / running_max

    def max_drawdown(self) -> float:
        """Maximum drawdown."""
        return self.drawdowns().min()

    def avg_drawdown(self) -> float:
        """Average drawdown."""
        dd = self.drawdowns()
        return dd[dd < 0].mean() if (dd < 0).any()