QuantLib.Stochastic
- class Discretize b where
- dDrift :: StochasticProcess a => a -> b -> Dot -> Double
- dDiff :: StochasticProcess a => a -> b -> Dot -> Double
- dDt :: StochasticProcess a => a -> b -> Dot -> Double
- class StochasticProcess a where
- data Dot = Dot {}
- type Path = [Dot]
- generatePath :: (StochasticProcess a, NormalGenerator b, Discretize c) => b -> c -> a -> Int -> Dot -> IO Path
- data GeometricBrownian = GeometricBrownian {}
- data ItoProcess = ItoProcess {}
- data SquareRootProcess = SquareRootProcess {}
- data OrnsteinUhlenbeckProcess = OrnsteinUhlenbeckProcess {}
- data BlackScholesProcess = BlackScholesProcess {
- bspRiskFree :: Double -> Double
- bspDividend :: Double -> Double
- bspBlackVol :: Dot -> Double
- data Euler = Euler {}
- data EndEuler = EndEuler {}
- data BoxMuller
- createNormalGen :: RNG -> BoxMuller
- mkNormalGen :: IO BoxMuller
- class NormalGenerator a where
- module GSL.Random.Gen
Documentation
class Discretize b whereSource
Discretization of stochastic process over given interval
Methods
dDrift :: StochasticProcess a => a -> b -> Dot -> DoubleSource
dDiff :: StochasticProcess a => a -> b -> Dot -> DoubleSource
dDt :: StochasticProcess a => a -> b -> Dot -> DoubleSource
Instances
class StochasticProcess a whereSource
1D Stochastic process
Dot. t and x pair
generatePath :: (StochasticProcess a, NormalGenerator b, Discretize c) => b -> c -> a -> Int -> Dot -> IO PathSource
Generates sample path for given stochastic process under discretization and normal generator for given amount of steps, starting from x0
data SquareRootProcess Source
Square-root process
data OrnsteinUhlenbeckProcess Source
Ornstein-Uhlenbeck process
data BlackScholesProcess Source
Generalized Black-Scholes process
Constructors
| BlackScholesProcess | |
Fields
| |
Instances
Euler discretization of stochastic processes
Euler end-point discretization of stochastic processes
createNormalGen :: RNG -> BoxMullerSource
Creates normally distributed generator
module GSL.Random.Gen