Class JumpRobustVolatility

java.lang.Object
com.quantfinlib.microstructure.JumpRobustVolatility
public final class JumpRobustVolatility extends Object
Jump-robust streaming volatility. A squared-return estimator (the one inside SignalEngine) cannot tell a news gap from diffusion: one headline print enters as r² and reads as a volatility regime shift for the estimator's whole memory. Bipower variation (Barndorff-Nielsen & Shephard, 2004) fixes this with a beautifully simple trick: use the product of consecutive absolute returns, (π/2)·|rₜ|·|rₜ₋₁|, instead of r². Diffusion moves both factors together, so the product estimates the same σ²; a single jump inflates only one factor of two neighboring products instead of one whole squared term — its weight in the estimate collapses.

Both estimators run side by side on time-decayed rates per second. volPerSqrtSecond() is the jump-robust (bipower) volatility, the one to feed VolatilityCurve and any model that should read regimes, not headlines. rawVolPerSqrtSecond() is the squared-return volatility, and jumpFraction() is the share of raw variance the robust estimator attributes to jumps (1 − BV/RV, clamped to [0,1]).

Gap discipline: a non-finite return or non-positive Δt drops the sample and resets the consecutive-return pairing, because multiplying across a feed gap would pair returns that were never neighbors. The first return after a gap therefore updates only the raw estimator; the bipower leg resumes one sample later. Irregular sampling is handled exactly: the two-return product is normalized by √(Δtₜ·Δtₜ₋₁) (each |r| scales with √ of its own interval), so event-time feeds, where activity accelerates precisely when volatility bursts, do not bias the estimator. Zero allocation per event, single writer, one instance per symbol.

  • Constructor Details

    • JumpRobustVolatility

      public JumpRobustVolatility(long halfLifeNanos)
      Parameters:
      halfLifeNanos - decay half-life, e.g. 10s = 10_000_000_000L

    • JumpRobustVolatility

      public JumpRobustVolatility()
      10-second half-life.

  • Method Details

    • onReturn

      public void onReturn(double ret, long dtNanos)
      One return observation: the relative mid change over the elapsed dtNanos. Non-finite returns or non-positive gaps drop the sample and break the pairing (see class doc).

    • volPerSqrtSecond

      public double volPerSqrtSecond()
      The jump-robust volatility, as return per √second — the diffusion component, with jumps down-weighted. 0 until two consecutive valid returns exist.

    • rawVolPerSqrtSecond

      public double rawVolPerSqrtSecond()
      The plain squared-return volatility (jumps and all), per √second.

    • jumpFraction

      public double jumpFraction()
      The share of raw variance attributed to jumps: clamp(1 − bipower/raw, 0, 1). Near 0 in pure diffusion, spikes after a discontinuous move, and decays back as the jump washes out of the raw estimator's memory. 0 while either estimator is unlearned.

    • samples

      public long samples()