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Vol Regimes

Vol regimes are persistent market states characterized by particular volatility levels and behaviors.

Definition​

A vol regime is a period where volatility exhibits consistent characteristics:

  • Level (high/low)
  • Behavior (trending, mean-reverting, jumping)
  • Dynamics (how it responds to spot moves)

Common Regimes​

RegimeBTC IV RangeCharacteristics
Low Vol30-45%Range-bound, grinding, low realized vol
Normal Vol45-65%Healthy trends, moderate swings
High Vol65-90%Fast moves, elevated fear
Crisis Vol90%+Panic, capitulation, gaps

Volatility Clustering​

One of the most robust empirical findings: volatility clusters. High vol days follow high vol days; low follows low.

GARCH Representation​

The standard model for vol clustering:

σt2=ω+αϵt−12+βσt−12\sigma_t^2 = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2

Where:

  • ω\omega = Long-run variance contribution
  • α\alpha = Shock impact (ARCH term)
  • β\beta = Persistence (GARCH term)

High α+β\alpha + \beta (near 1) means high persistence.

Mean Reversion​

Despite clustering, vol tends to revert to long-term levels.

Speed of Reversion​

Measured by the mean-reversion parameter κ\kappa:

dσ=κ(σˉ−σ)dt+noised\sigma = \kappa(\bar{\sigma} - \sigma)dt + \text{noise}

Crypto vol reverts faster than equity vol (higher κ\kappa).

Implications​

  • Extreme vol readings don't persist
  • After spikes, expect gradual decline
  • After calm periods, expect eventual increase

Volatility Risk Premium (VRP)​

The tendency for IV to exceed subsequent RV on average.

VRP=E[IV]−E[RV]\text{VRP} = \mathbb{E}[\text{IV}] - \mathbb{E}[\text{RV}]

Historical VRP by Market​

MarketTypical VRPNotes
SPX2-4 vol pointsVery consistent
BTC5-15 vol pointsHigher and variable
ETH5-20 vol pointsSimilar to BTC

VRP Dynamics​

VRP varies by regime:

  • Low vol: VRP compressed or negative
  • Post-spike: VRP often very high
  • Normal: Moderate positive VRP

Regime Detection​

Simple Methods​

  1. Percentile ranking: Where is current IV vs history?
  2. IV/RV ratio: Is IV rich or cheap?
  3. Term structure shape: Backwardation suggests high near-term risk

Statistical Methods​

  1. Rolling statistics: 20-day realized vol percentile
  2. Markov regime switching: Formal state estimation
  3. Hidden Markov Models: Probabilistic regime identification

Trading in Different Regimes​

RegimeLong VolShort VolKey Risk
Low VolCheap but bleedsWorks but exposed to spikesSudden transition
NormalFairFairNo edge
High VolExpensiveRiskyContinued elevation
CrisisVery expensiveDangerousAnything goes

Building intuition​

Learn vol regimes from scratchInteractive lesson · no prerequisites

The interactive lesson above covers vol regimes from first principles: what vol regimes are, low-vol compression, high-vol expansion and crisis, and mean reversion with the kappa parameter and half-life formula.

Open source implementations​

RepoWhy inspect it
archGARCH/EGARCH vol clustering models in Python
QuantLibVol models and mean-reversion

See Also​

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