Safe Haskell	None
Language	Haskell2010

QuantLib.Stochastic

Synopsis

data PureMT
newPureMT :: IO PureMT
randomDouble :: PureMT -> (Double, PureMT)
splitMT :: PureMT -> (PureMT, PureMT)
splitMTwithSeed :: Integer -> PureMT -> (PureMT, PureMT)
data BoxMuller a
mkNormalGen :: RandomGenerator a => IO (BoxMuller a)
class NormalGenerator a where
- ngGetNext :: a -> (Double, a)
- ngMkNew :: a -> IO a
- ngSplit :: a -> (a, a)
- ngSplitWithSeed :: Integer -> a -> (a, a)
data InverseNormal a
mkInverseNormal :: RandomGenerator a => IO (InverseNormal a)
data EndEuler = EndEuler {
- eeDt :: Double
}
data Euler = Euler {
- eDt :: Double
}
data BlackScholesProcess = BlackScholesProcess {
- bspRiskFree :: Double -> Double
- bspDividend :: Double -> Double
- bspBlackVol :: Dot -> Double
}
data OrnsteinUhlenbeckProcess = OrnsteinUhlenbeckProcess {
- oupSpeed :: Double
- oupLevel :: Double
- oupSigma :: Double
}
data SquareRootProcess = SquareRootProcess {
- srpSpeed :: Double
- srpMean :: Double
- srpSigma :: Double
}
data ItoProcess = ItoProcess {
- ipDrift :: Dot -> Double
- ipDiff :: Dot -> Double
}
data GeometricBrownian = GeometricBrownian {
- gbDrift :: Double
- gbDiff :: Double
}
type Path = [Dot]
data Dot = Dot {
- getT :: !Double
- getX :: !Double
}
class StochasticProcess a where
- drift :: a -> Dot -> Double
- diff :: a -> Dot -> Double
- evolve :: Discretize b => b -> a -> Dot -> Double -> Dot
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
generatePath :: (StochasticProcess a, NormalGenerator b, Discretize c) => b -> c -> a -> Int -> Dot -> Path

Documentation

data PureMT Source #

newPureMT :: IO PureMT Source #

randomDouble :: PureMT -> (Double, PureMT) Source #

splitMT :: PureMT -> (PureMT, PureMT) Source #

splitMTwithSeed :: Integer -> PureMT -> (PureMT, PureMT) Source #

data BoxMuller a Source #

Box-Muller method

Instances

Instances details

RandomGenerator a => NormalGenerator (BoxMuller a) Source #
Instance details Defined in QuantLib.Stochastic.Random Methods ngGetNext :: BoxMuller a -> (Double, BoxMuller a) Source # ngMkNew :: BoxMuller a -> IO (BoxMuller a) Source # ngSplit :: BoxMuller a -> (BoxMuller a, BoxMuller a) Source # ngSplitWithSeed :: Integer -> BoxMuller a -> (BoxMuller a, BoxMuller a) Source #

mkNormalGen :: RandomGenerator a => IO (BoxMuller a) Source #

class NormalGenerator a where Source #

Normally distributed generator

Minimal complete definition

ngGetNext, ngMkNew, ngSplitWithSeed

Methods

ngGetNext :: a -> (Double, a) Source #

ngMkNew :: a -> IO a Source #

ngSplit :: a -> (a, a) Source #

ngSplitWithSeed :: Integer -> a -> (a, a) Source #

Instances

Instances details

RandomGenerator a => NormalGenerator (InverseNormal a) Source #
Instance details Defined in QuantLib.Stochastic.Random Methods ngGetNext :: InverseNormal a -> (Double, InverseNormal a) Source # ngMkNew :: InverseNormal a -> IO (InverseNormal a) Source # ngSplit :: InverseNormal a -> (InverseNormal a, InverseNormal a) Source # ngSplitWithSeed :: Integer -> InverseNormal a -> (InverseNormal a, InverseNormal a) Source #
RandomGenerator a => NormalGenerator (BoxMuller a) Source #
Instance details Defined in QuantLib.Stochastic.Random Methods ngGetNext :: BoxMuller a -> (Double, BoxMuller a) Source # ngMkNew :: BoxMuller a -> IO (BoxMuller a) Source # ngSplit :: BoxMuller a -> (BoxMuller a, BoxMuller a) Source # ngSplitWithSeed :: Integer -> BoxMuller a -> (BoxMuller a, BoxMuller a) Source #

data InverseNormal a Source #

Normal number generation using inverse cummulative normal distribution

Instances

Instances details

RandomGenerator a => NormalGenerator (InverseNormal a) Source #
Instance details Defined in QuantLib.Stochastic.Random Methods ngGetNext :: InverseNormal a -> (Double, InverseNormal a) Source # ngMkNew :: InverseNormal a -> IO (InverseNormal a) Source # ngSplit :: InverseNormal a -> (InverseNormal a, InverseNormal a) Source # ngSplitWithSeed :: Integer -> InverseNormal a -> (InverseNormal a, InverseNormal a) Source #

mkInverseNormal :: RandomGenerator a => IO (InverseNormal a) Source #

data EndEuler Source #

Euler end-point discretization of stochastic processes

Constructors

EndEuler
Fields eeDt :: Double

Instances

Instances details

Eq EndEuler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods (==) :: EndEuler -> EndEuler -> Bool # (/=) :: EndEuler -> EndEuler -> Bool #
Show EndEuler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods showsPrec :: Int -> EndEuler -> ShowS # show :: EndEuler -> String # showList :: [EndEuler] -> ShowS #
Discretize EndEuler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods dDrift :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source # dDiff :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source # dDt :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source #

data Euler Source #

Euler discretization of stochastic processes

Constructors

Euler
Fields eDt :: Double

Instances

Instances details

Eq Euler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods (==) :: Euler -> Euler -> Bool # (/=) :: Euler -> Euler -> Bool #
Show Euler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods showsPrec :: Int -> Euler -> ShowS # show :: Euler -> String # showList :: [Euler] -> ShowS #
Discretize Euler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods dDrift :: StochasticProcess a => a -> Euler -> Dot -> Double Source # dDiff :: StochasticProcess a => a -> Euler -> Dot -> Double Source # dDt :: StochasticProcess a => a -> Euler -> Dot -> Double Source #

data BlackScholesProcess Source #

Generalized Black-Scholes process

Constructors

BlackScholesProcess
Fields bspRiskFree :: Double -> Double bspDividend :: Double -> Double bspBlackVol :: Dot -> Double

Instances

Instances details

StochasticProcess BlackScholesProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: BlackScholesProcess -> Dot -> Double Source # diff :: BlackScholesProcess -> Dot -> Double Source # evolve :: Discretize b => b -> BlackScholesProcess -> Dot -> Double -> Dot Source #

data OrnsteinUhlenbeckProcess Source #

Ornstein-Uhlenbeck process

Constructors

OrnsteinUhlenbeckProcess
Fields oupSpeed :: Double oupLevel :: Double oupSigma :: Double

Instances

Instances details

Show OrnsteinUhlenbeckProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods showsPrec :: Int -> OrnsteinUhlenbeckProcess -> ShowS # show :: OrnsteinUhlenbeckProcess -> String # showList :: [OrnsteinUhlenbeckProcess] -> ShowS #
StochasticProcess OrnsteinUhlenbeckProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: OrnsteinUhlenbeckProcess -> Dot -> Double Source # diff :: OrnsteinUhlenbeckProcess -> Dot -> Double Source # evolve :: Discretize b => b -> OrnsteinUhlenbeckProcess -> Dot -> Double -> Dot Source #

data SquareRootProcess Source #

Square-root process

Constructors

SquareRootProcess
Fields srpSpeed :: Double srpMean :: Double srpSigma :: Double

Instances

Instances details

Show SquareRootProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods showsPrec :: Int -> SquareRootProcess -> ShowS # show :: SquareRootProcess -> String # showList :: [SquareRootProcess] -> ShowS #
StochasticProcess SquareRootProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: SquareRootProcess -> Dot -> Double Source # diff :: SquareRootProcess -> Dot -> Double Source # evolve :: Discretize b => b -> SquareRootProcess -> Dot -> Double -> Dot Source #

data ItoProcess Source #

Ito process

Constructors

ItoProcess
Fields ipDrift :: Dot -> Double ipDiff :: Dot -> Double

Instances

Instances details

StochasticProcess ItoProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: ItoProcess -> Dot -> Double Source # diff :: ItoProcess -> Dot -> Double Source # evolve :: Discretize b => b -> ItoProcess -> Dot -> Double -> Dot Source #

data GeometricBrownian Source #

Geometric Brownian motion

Constructors

GeometricBrownian
Fields gbDrift :: Double gbDiff :: Double

Instances

Instances details

Show GeometricBrownian Source #
Instance details Defined in QuantLib.Stochastic.Process Methods showsPrec :: Int -> GeometricBrownian -> ShowS # show :: GeometricBrownian -> String # showList :: [GeometricBrownian] -> ShowS #
StochasticProcess GeometricBrownian Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: GeometricBrownian -> Dot -> Double Source # diff :: GeometricBrownian -> Dot -> Double Source # evolve :: Discretize b => b -> GeometricBrownian -> Dot -> Double -> Dot Source #

type Path = [Dot] Source #

Path as list of Dots

data Dot Source #

Dot. t and x pair

Constructors

Dot
Fields getT :: !Double getX :: !Double

Instances

Instances details

Eq Dot Source #
Instance details Defined in QuantLib.Stochastic.Process Methods (==) :: Dot -> Dot -> Bool # (/=) :: Dot -> Dot -> Bool #
Show Dot Source #
Instance details Defined in QuantLib.Stochastic.Process Methods showsPrec :: Int -> Dot -> ShowS # show :: Dot -> String # showList :: [Dot] -> ShowS #

class StochasticProcess a where Source #

1D Stochastic process

Minimal complete definition

drift, diff

Methods

drift :: a -> Dot -> Double Source #

diff :: a -> Dot -> Double Source #

evolve :: Discretize b => b -> a -> Dot -> Double -> Dot Source #

Instances

Instances details

StochasticProcess BlackScholesProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: BlackScholesProcess -> Dot -> Double Source # diff :: BlackScholesProcess -> Dot -> Double Source # evolve :: Discretize b => b -> BlackScholesProcess -> Dot -> Double -> Dot Source #
StochasticProcess OrnsteinUhlenbeckProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: OrnsteinUhlenbeckProcess -> Dot -> Double Source # diff :: OrnsteinUhlenbeckProcess -> Dot -> Double Source # evolve :: Discretize b => b -> OrnsteinUhlenbeckProcess -> Dot -> Double -> Dot Source #
StochasticProcess SquareRootProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: SquareRootProcess -> Dot -> Double Source # diff :: SquareRootProcess -> Dot -> Double Source # evolve :: Discretize b => b -> SquareRootProcess -> Dot -> Double -> Dot Source #
StochasticProcess ItoProcess Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: ItoProcess -> Dot -> Double Source # diff :: ItoProcess -> Dot -> Double Source # evolve :: Discretize b => b -> ItoProcess -> Dot -> Double -> Dot Source #
StochasticProcess GeometricBrownian Source #
Instance details Defined in QuantLib.Stochastic.Process Methods drift :: GeometricBrownian -> Dot -> Double Source # diff :: GeometricBrownian -> Dot -> Double Source # evolve :: Discretize b => b -> GeometricBrownian -> Dot -> Double -> Dot Source #

class Discretize b where Source #

Discretization of stochastic process over given interval

Methods

dDrift :: StochasticProcess a => a -> b -> Dot -> Double Source #

dDiff :: StochasticProcess a => a -> b -> Dot -> Double Source #

dDt :: StochasticProcess a => a -> b -> Dot -> Double Source #

Instances

Instances details

Discretize EndEuler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods dDrift :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source # dDiff :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source # dDt :: StochasticProcess a => a -> EndEuler -> Dot -> Double Source #
Discretize Euler Source #
Instance details Defined in QuantLib.Stochastic.Discretize Methods dDrift :: StochasticProcess a => a -> Euler -> Dot -> Double Source # dDiff :: StochasticProcess a => a -> Euler -> Dot -> Double Source # dDt :: StochasticProcess a => a -> Euler -> Dot -> Double Source #

generatePath :: (StochasticProcess a, NormalGenerator b, Discretize c) => b -> c -> a -> Int -> Dot -> Path Source #

Generates sample path for given stochastic process under discretization and normal generator for given amount of steps, starting from x0