StochasticProcess
  ExtOUWithJumpsProcess
  GJRGARCHProcess
  HybridHestonHullWhiteProcess
  KlugeExtOUProcess
  LiborForwardModelProcess
  StochasticProcessArray
  G2Process
  G2ForwardProcess
  HestonProcess
    BatesProcess
  HestonSLVProcess
  StochasticProcess1D
    ExtendedOrnsteinUhlenbeckProcess
    HullWhiteForwardProcess
    HullWhiteProcess
    Merton76Process
    VarianceGammaProcess
    GeneralizedBlackScholesProcess
      BlackProcess

The root of the hierarchy shown under GenStochasticProcess.

A StochasticProcess1D or one of its leaves; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

A GeneralizedBlackScholesProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A HestonProcess or one of its leaves; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A HestonProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

A StochasticProcess1D; see the hierarchy under GenStochasticProcess.

Black (1976) process for a forward or futures contract: d(ln S) = -sigma^2/2 dt + sigma dW.

Merton (1973) extension of Black-Scholes for a continuous-dividend-paying stock: d(ln S) = (r - q - sigma^2/2) dt + sigma dW.

Black-Scholes (1973) process for a stock: d(ln S) = (r - sigma^2/2) dt + sigma dW.

blackScholesMertonProcess with a choice of evolution scheme (Euler/Milstein/predictor-corrector) on top of the discretization argument.

Garman-Kohlhagen (1983) process for an exchange rate: d(ln S) = (r - r_f - sigma^2/2) dt + sigma dW.

Generalized Black-Scholes process with separate dividend and risk-free curves: d(ln S) = (r - q - sigma^2/2) dt + sigma dW.

square-root process: dx = a (b - x) dt + sigma sqrt(x) dW.

blackScholesMertonProcess variant supporting local vega stress tests over a given time/asset border and stress level.

square-root stochastic-volatility Bates process: a Heston process plus a compound Poisson jump component with log-normally distributed jump size.

Kluge model: an extended Ornstein-Uhlenbeck process plus an exponential-jump component, S = exp(X + Y) with dX = alpha (mu(t) - X) dt + sigma dW and dY = -beta Y dt + J dN. When the extended process is callback-backed, this result must remain inside the same continuation.

T-forward-measure counterpart of g2Process: the two-factor G2++ short-rate model, with the simulated state again shifted so its components sum to the short rate.

two-factor G2++ short-rate process, state shifted so its two OU components sum to the short rate; degenerates to a pair of zero-mean OU processes if no term structure is given.

Geman-Roncoroni process, a mean-reverting jump-diffusion model for electricity spot prices with a seasonal deterministic mean and an asymmetric jump term.

geometric Brownian motion process: dS = mue S dt + sigma S dW.

stochastic-volatility GJR-GARCH(1,1) process; parameters are supplied as daily constants and annualized internally via daysPerYear.

dividendYield may be Nothing (an empty term-structure handle) -- required e.g. by integralHestonVarianceOptionEngine, which rejects a process with a non-empty dividend handle.

Two-factor Heston stochastic-local-volatility process using the supplied calibrated leverage function. It is a generic StochasticProcess, so it composes with path generators and the existing drift/diffusion operations.

T-forward-measure counterpart of hullWhiteProcess.

Hull-White one-factor short-rate process, fitted to the given initial term structure.

three-factor hybrid model combining a Heston equity process with a Hull-White short-rate process, correlated via corrEquityShortRate.

Joint correlated Kluge (extOuWithJumpsProcess) and extended Ornstein-Uhlenbeck process. When either input retains a Haskell callback, this result must remain inside that callback's scope.

An extended Ornstein-Uhlenbeck process dx = speed*(b(t) - x)dt + sigma*dW with an arbitrary deterministic mean-reversion level b, usable with pathGenerator. The FunPtr backing b is kept alive only for the continuation's duration. The process and any dependent process must not escape it (same rule as withDerivedQuote).

An extended Ornstein-Uhlenbeck process with the Lucia-Schwartz-style level b(t) = a + k*t + c*sin(2*pi*t + phase), computed natively. Setting k = c = 0 gives a constant level and c = 0 gives a linear trend.

Libor market model process, evolving size forward rates of index under the rolling forward measure with a predictor-corrector step.

Merton (1976) jump-diffusion process: a Black-Scholes process plus a log-normal jump component with Poisson jump intensity jumpInt.

Ornstein-Uhlenbeck process: dx = a (level - x) dt + sigma dW.

Variance Gamma process: a Brownian motion db = theta dt + sigma dW time-changed by an independent Gamma process with mean 1 and variance rate nu.

array of correlated 1-D stochastic processes, driven by a joint correlation matrix.

Forward-measure processes that require a maturity time before simulation.

applies an increment dx to a state x0 in the process's own state space -- not always plain addition (a log-variable process exponentiates, for instance), which is why it is a method rather than a caller-side zipWith (+).

the covariance matrix of the process over dt given state x0 at time t0; equals stdDeviation * transpose stdDeviation.

the diffusion part of the process's SDE at state x and time t, i.e. sigma(t, x_t) in dx_t = mu(t, x_t) dt + sigma(t, x_t) dW_t.

the drift part of the process's SDE at state x and time t, i.e. mu(t, x_t) in dx_t = mu(t, x_t) dt + sigma(t, x_t) dW_t.

one discretized step of the process: the state at t0+dt given state x0 at t0 and the standard-normal draws dw (one per factors).

This crosses the FFI boundary once per timestep, so it is not the way to generate many paths -- use pathGenerator for that, which drives the same evolve entirely inside QuantLib. Reach for this when Haskell has to own the path logic (path-dependent state, early termination, a custom draw source via gaussianRsg) while QuantLib keeps owning the process's discretization scheme.

E[x_(t0+dt) | x_t0 = x0], the expected state at t0+dt given state x0 at time t0.

the standard deviation matrix of the process over dt given state x0 at time t0, i.e. a square root of covariance -- the factor a simulated normal draw is multiplied by in one exact-scheme step. Pairs with expectation: x_(t0+dt) = expectation + stdDeviation * dw for a process whose discretization is exact in that sense.

Probability density of (x = ln S, v) at time t, evaluated via the Heston characteristic-function inversion (the same machinery as analyticPDFHestonEngine). eps is the requested integration accuracy.

the cumulative discount factors implied by one simulated vector of forward rates: element i discounts from the end of accrual period i back to the process's start, i.e. scanl1 (*) [1 / (1 + r_i tau_i)] over accrualTimes -- not the individual one-period factors.

the number of independent Brownian factors driving a stochastic process -- e.g. 2 for g2Process, matching its state size; used to size a pathGenerator's underlying sequence generator (process->factors() * steps, mirroring upstream's own usage).

the process's state at time 0, e.g. (0, 0) for a curveless g2Process or (phi(0), 0) once a term structure is given.

the reset (fixing) dates of the forward rates this process evolves

the reset (fixing) times of the forward rates this process evolves, in the process's own day count fraction from the evaluation date

the leg of Ibor coupons (notional amount each) this process's forward rates reset -- used e.g. to build the cap this process prices via liborForwardModel

the underlying IborIndex this process was constructed with

the (start, end) accrual times of the forward rates this process evolves, in its own day count fraction from the evaluation date; their difference is the accrual period a caplet payoff is scaled by.

Two-factor short-rate processes with the same deterministic curve-fitting offset.

Two-factor processes whose simulated state maps to the short rate in the same way.

Hull--White processes exposing the deterministic curve-fitting offset.

the Hull-White B(t, T) = (1 - exp(-a (T - t))) / a factor of the affine discount-bond formula P(t, T) = A(t, T) exp(-B(t, T) r_t).

the T-forward-measure drift adjustment M_T(s, t, T) the process applies between s and t when the numeraire is the T-maturity zero bond.

the T-forward-measure numeraire of a hybridHestonHullWhiteProcess at time t in state x: P(t, T, x!!2) / P(0, T), where T is the process's forward-measure time and P is the Hull-White discount bond implied by the simulated short-rate factor.

The state is [S, v, r] -- spot, variance and the Hull-White factor, in the order initialValues returns -- and only x!!2 is read. This is not a bank account: to turn a simulated time-t payoff into a present value, divide it by this numeraire and multiply by the curve's own discount ts T.

T is captured when the hybrid process is constructed, from the hullWhiteForwardProcess's forward-measure time -- so setForwardMeasureTime must be called on that process before hybridHestonHullWhiteProcess, not after.

default theta calculation for Black-Scholes options