volatility-modeling

What this skill does

# Volatility Modeling

## Purpose
Model, forecast, and interpret volatility using time-series models and options-implied measures. This skill covers EWMA and GARCH(1,1) for volatility forecasting, implied volatility extraction, volatility smile/skew/surface construction, the volatility term structure, the realized-vs-implied volatility gap (volatility risk premium), and the VIX index. These tools are foundational for options pricing, risk management, and trading strategy development.

## Layer
1b — Forward-Looking Risk

## Direction
Prospective

## When to Use
- Forecasting future volatility for risk management or position sizing
- Building EWMA or GARCH models to capture volatility clustering and mean reversion
- Extracting implied volatility from option prices using Black-Scholes or other models
- Constructing or interpreting volatility smiles, skews, and surfaces
- Analyzing the volatility term structure across different maturities
- Comparing realized (historical) volatility to implied volatility to assess the volatility risk premium
- Understanding VIX and its relationship to market sentiment and expected risk

## Core Concepts

### EWMA (Exponentially Weighted Moving Average)
A simple volatility model that gives more weight to recent observations. RiskMetrics popularized this approach with a standard decay factor.

```
sigma^2_t = lambda * sigma^2_{t-1} + (1 - lambda) * r^2_{t-1}
```

where:
- lambda = decay factor (RiskMetrics standard: 0.94 for daily, 0.97 for monthly)
- r_{t-1} = return in period t-1 (typically demeaned, but for daily returns the mean is often assumed to be zero)
- sigma^2_{t-1} = previous period's variance estimate

**Properties:**
- Assigns exponentially decaying weights to past squared returns.
- Effective window is approximately 1/(1 - lambda) observations. For lambda = 0.94, effective window is approximately 17 days.
- No mean reversion: the model is equivalent to IGARCH (integrated GARCH) where alpha + beta = 1. Volatility shocks persist indefinitely.
- Simple to implement and requires only one parameter.

### GARCH(1,1)
The Generalized Autoregressive Conditional Heteroskedasticity model adds a constant term that induces mean reversion in volatility.

```
sigma^2_t = omega + alpha * r^2_{t-1} + beta * sigma^2_{t-1}
```

where:
- omega > 0: constant term (determines long-run variance level)
- alpha >= 0: reaction coefficient (sensitivity to recent shocks)
- beta >= 0: persistence coefficient (memory of past variance)

**Stationarity condition:** alpha + beta < 1. This ensures the process is covariance-stationary and mean-reverting.

**Long-run (unconditional) variance:**

```
V_L = omega / (1 - alpha - beta)
```

Long-run annualized volatility: sigma_L = sqrt(V_L * 252).

**Persistence:** The quantity alpha + beta measures how quickly volatility reverts to its long-run level. Higher persistence means slower mean reversion.

**Half-life of volatility shocks:** The number of periods for a volatility shock to decay by half:

```
h = -ln(2) / ln(alpha + beta)
```

Since alpha + beta < 1, ln(alpha + beta) < 0, and h is positive.

**Multi-step forecasts:** The h-step-ahead GARCH(1,1) forecast:

```
E[sigma^2_{t+h}] = V_L + (alpha + beta)^h * (sigma^2_t - V_L)
```

The forecast converges to V_L as h approaches infinity.

### Implied Volatility
The volatility value that, when plugged into an option pricing model (typically Black-Scholes), produces a theoretical price equal to the observed market price.

For a European call under Black-Scholes:

```
C = S * N(d1) - K * exp(-rT) * N(d2)

d1 = [ln(S/K) + (r + sigma^2/2) * T] / (sigma * sqrt(T))
d2 = d1 - sigma * sqrt(T)
```

Implied volatility is the sigma that solves C_model(sigma) = C_market. There is no closed-form solution; it must be found numerically (e.g., Newton-Raphson, bisection).

### Volatility Smile and Skew
In practice, implied volatility varies by strike price, contradicting the constant-volatility assumption of Black-Scholes.

- **Volatility smile:** IV is higher for both deep in-the-money and deep out-of-the-money options, forming a U-shape. Common in FX markets.
- **Volatility skew (smirk):** IV increases for lower strikes (OTM puts have higher IV than OTM calls). This is the dominant pattern in equity markets and reflects demand for downside protection and the reality of fat left tails.
- **Skew is often quantified** as the difference in IV between a 25-delta put and a 25-delta call, or between 90% moneyness and 110% moneyness strikes.

### Volatility Term Structure
Implied volatility varies across option expiration dates.

- **Normal (upward-sloping):** Longer-dated options have higher IV. Reflects uncertainty increasing over time.
- **Inverted (downward-sloping):** Near-term IV exceeds long-term IV. Common during market stress when short-term uncertainty spikes (e.g., around earnings, elections, crises).
- **Humped:** IV peaks at an intermediate maturity. May occur around a specific anticipated event.

### Volatility Surface
The two-dimensional surface of implied volatility across both strike (or delta/moneyness) and maturity. The volatility surface is the most complete representation of the options market's view of future uncertainty.

Practitioners interpolate the surface to price options at arbitrary strike/maturity combinations. Surface dynamics (how the surface shifts, tilts, and bends) are critical for options portfolio risk management.

### Realized vs Implied Volatility: The Volatility Risk Premium
Implied volatility systematically exceeds subsequent realized volatility on average. This gap is the **volatility risk premium (VRP)**.

```
VRP = IV - RV_subsequent
```

The VRP exists because investors are willing to pay a premium for options (insurance), and option sellers demand compensation for bearing tail risk. The VRP is typically positive and has been a persistent source of return for volatility sellers.

Key considerations:
- The VRP varies over time and is larger during periods of market stress.
- Selling volatility (harvesting VRP) earns a steady premium but is exposed to large, infrequent losses.
- The VRP can turn negative during extreme events.

### VIX Index
The CBOE Volatility Index measures the market's expectation of 30-day forward volatility, derived from S&P 500 option prices.

- VIX is quoted in annualized percentage points (e.g., VIX = 20 means approximately 20% expected annualized vol).
- VIX is computed from a wide strip of OTM put and call options, not from the Black-Scholes model.
- VIX levels: 12-15 = low/complacent, 15-20 = normal, 20-30 = elevated, 30+ = high stress, 40+ = crisis.
- VIX has strong mean-reverting properties and tends to spike during market selloffs ("fear gauge").

## Key Formulas

| Formula | Expression | Use Case |
|---------|-----------|----------|
| EWMA Variance | sigma^2_t = lambda * sigma^2_{t-1} + (1-lambda) * r^2_{t-1} | Simple volatility forecast |
| GARCH(1,1) Variance | sigma^2_t = omega + alpha * r^2_{t-1} + beta * sigma^2_{t-1} | Mean-reverting vol forecast |
| GARCH Long-Run Variance | V_L = omega / (1 - alpha - beta) | Unconditional variance level |
| GARCH Half-Life | h = -ln(2) / ln(alpha + beta) | Speed of mean reversion |
| GARCH h-Step Forecast | V_L + (alpha+beta)^h * (sigma^2_t - V_L) | Multi-period vol forecast |
| Black-Scholes Call | S * N(d1) - K * exp(-rT) * N(d2) | Option pricing (IV extraction) |
| Volatility Risk Premium | IV - RV_subsequent | Premium earned by vol sellers |
| EWMA Effective Window | approximately 1 / (1 - lambda) | Implicit lookback period |

## Worked Examples

### Example 1: EWMA Variance Update
**Given:** Yesterday's variance estimate sigma^2_{t-1} = 0.0004 (daily vol = 2%), yesterday's return r_{t-1} = -3% (i.e., r = -0.03), and lambda = 0.94.

**Calculate:** Today's EWMA variance estimate and daily volatility.

**Solution:**

```
sigma^2_t = 0.94 * 0.0004 + (1 - 0.94) * (-0.03)^2
          = 0.94 * 0.0004 + 0.06 * 0.0009
          = 0.000376 + 0.000054