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ConstantVolatilityAsset.toString()
methods.SFSMarkovChain.a(int).
dC/dS computed from discounted analytic price
* C(t) Implementation at this level
* is an error message and program exit.
T_n-forward price at
discrete time t (continuous time T_t).
T_n-forward price at time t.
T_n-forward swap price
c(t)=B_pq(t)(S_pq(t)-kappa)/B_n(t) at time t.
T_n-forward price at time t.
d^2C/dS^2 computed from discounted analytic price
* C(t).
Option.minimumVarianceDelta(int, int, int) hedge
* weights.
Option.quotientDelta(int, int) hedge
* weights.
S(t)*(dDelta/dS)(t).
S(t)*Gamma(t).
dC/dt computed from discounted analytic price
* C(t).
dDelta/dt computed from discounted analytic
* price C(t).
dC/dsigma computed from discounted analytic price
* C(t).
B_i(T_t)=B(s,T) with
s=T_t,T=T_i.
B(t,T) with t<=T
arbitrary times.
B_i(0)=B(0,T_i).
B_i(0)=B(0,T_i).
fullInitialization is
set to true.
- BLACK -
Static variable in class Graphics.PointFrame
-
- BLUE -
Static variable in class Graphics.PointFrame
-
- BSWPNTest - class Libor.LiborDerivatives.BSWPNTest.
-
- BSWPNTest() -
Constructor for class Libor.LiborDerivatives.BSWPNTest
- Creates a new instance of BSWPNTest
- B_i0(int) -
Method in class Libor.LiborProcess.LiborVector
- The zero coupon bond
B_i(0)=B(0,T_i).
- B_iTm(int) -
Method in class Libor.LiborProcess.LiborVector
- The zero coupon bond
B_i(T_m).
- B_pq(int, int) -
Method in class Libor.LiborProcess.Calibrator
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t)
at time t=0.
- B_pq(int, int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t).
- B_pq(int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t)
at time t=0.
- B_pq0(int, int) -
Method in class Libor.LiborProcess.LiborVector
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t)
at time t=0.
- B_pqTm(int, int) -
Method in class Libor.LiborProcess.LiborVector
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t).
- BasicAssetPair - class Market.BasicAssetPair.
- Market consisting of two assets (excluding the riskfree bond) with
constant instantaneous volatility and correlation of returns.
- BasicAssetPair(int, double, double[], double, double[], double[], double, double[]) -
Constructor for class Market.BasicAssetPair
-
- BasicHistogram - class Statistics.BasicHistogram.
- Basic histogram of a random variable able to write ASCII character output
which can then be processed by Scigraphica or gri (a graphics programming
language) into a postscript histogram.
- BasicHistogram(RandomVariable, int, int, double) -
Constructor for class Statistics.BasicHistogram
- Constructor retrieves and bins values.
- BasicHistogram(RandomVariable, int, int, double, boolean) -
Constructor for class Statistics.BasicHistogram
- Constructor retrieves and bins values.
- Basket - class Market.Basket.
- Interface and default methods for all multi assets markets
(deterministic or stochastic volatility, deterministic or stochastic rates).
- Basket(int, double, double[], double[]) -
Constructor for class Market.Basket
- Constructor,
call from concrete subclass.
- BasketOption - class Options.BasketOption.
- Interface and default methods to price and hedge a possibly path
path dependent European option on a basket of underlying assets.
- BasketOption(Basket, String) -
Constructor for class Options.BasketOption
- Constructor, does not initialize the option price path.
- BermudanExerciseBoundary - class Libor.LiborDerivatives.BermudanExerciseBoundary.
- A JFrame associated with a
BermudanSwaption and a two
dimensional statistic (path functional) of the underlying Libor
path. - BermudanExerciseBoundary(String, BermudanSwaption, Trigger, int, int, double, double, double, double) -
Constructor for class Libor.LiborDerivatives.BermudanExerciseBoundary
- Constructor
- BermudanExerciseBoundary(String, BermudanSwaption, Trigger, int, int, double, double, double, double, double, double, Color) -
Constructor for class Libor.LiborDerivatives.BermudanExerciseBoundary
- Constructor, this one draws axes
x=x0, y=y0.
- BermudanSwaption - class Libor.LiborDerivatives.BermudanSwaption.
- Bermudan Swaption.
- BermudanSwaption(LiborProcess, int, double) -
Constructor for class Libor.LiborDerivatives.BermudanSwaption
- Constructor, underlying swap ends at the terminal date of the
underlying Libor process.
- BetaVariable - class RandomVariables.BetaVariable.
- Beta(alpha,beta) variable.
- BetaVariable(double, double) -
Constructor for class RandomVariables.BetaVariable
- Probability density
f(x)=Gamma(alpha+beta)x^(alpha-1)(1-x)^(beta-1)/
Gamma(alpha)Gamma(beta).
- BiasedRandomWalk - class Processes.BiasedRandomWalk.
- Random walk moving up 1 with probability p and down 1
* with probability 1-p.
- BiasedRandomWalk(int, double, double) -
Constructor for class Processes.BiasedRandomWalk
- Constructor
- BinomialVariable - class RandomVariables.BinomialVariable.
- Binomial B(n,p) variable X.
- BinomialVariable(int, double) -
Constructor for class RandomVariables.BinomialVariable
- Parameters of the binomial distribution:,/p>
- BisectionSolveBSF(double, double, double) -
Static method in class Statistics.FinMath
- Same as
FinMath.NewtonSolveBSF(double, double, double) but uses continued bisection instead of
* Newton's algorithm.
- BlackScholesCall - class Options.BlackScholesCall.
- Plain vanilla European call.
- BlackScholesCall(double, ConstantVolatilityAsset) -
Constructor for class Options.BlackScholesCall
-
- BondPaths - class Examples.Libor.BondPaths.
- Opens a window, allocates a Libor Process of dimension
n=60
with quarterly compounding and then displays zero coupon bond paths with
300 time steps to maturity. - BondPaths(LiborProcess, double, double, double, int) -
Constructor for class Examples.Libor.BondPaths
- Some parameter values are needed for the super class constructor
(
T,B0).
- BrownianMotion - class Processes.BrownianMotion.
- A one dimensional standard Brownian motion
*
* @author Michael J.
- BrownianMotion(int, double, double) -
Constructor for class Processes.BrownianMotion
- Constructor
*
* @param T Number of time steps to horizon.
- BuyAndHold - class Examples.Trading.BuyAndHold.
- Console program allocating a constant volatility asset and examining
the buy and hold strategy.
- BuyAndHold() -
Constructor for class Examples.Trading.BuyAndHold
-
- b -
Static variable in class Examples.Probability.ExpectationTest
-
- b(int) -
Method in class Processes.SFSMarkovChain
- Upper bound for states.
- b(int) -
Method in class Processes.SFSMarkovChainImpl
- Defines
SFSMarkovChain.b(int).
- b(int) -
Method in class Processes.SFSStoppableMarkovChain
- Upper bound for states.
- b(int, int) -
Method in class Processes.StoppableMarkovChain
- Upper bound for states.
- b_pq(int, int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The annuity
B_pq(t)=sum_{k=p}^{q-1}delta_kB_{k+1}(t).
- bad() -
Method in class com.skylit.io.EasyReader
- Checks the status of the file
* @return true if en error occurred opening or reading the file,
* false otherwise
- bad() -
Method in class com.skylit.io.EasyWriter
- Checks the status of the file
* @return true if en error occurred opening or writing to the file,
* false otherwise
- basicHistogram(int, int, double, boolean) -
Method in class Statistics.RandomVariable
- Returns a
BasicHistogram (for plotting with Scigraphica
or the Gri graphing language).
- beta -
Variable in class Libor.LiborProcess.Calibrator
- CS_FactorLoading parameter
- betaCoefficient(int, int) -
Method in class Statistics.ControlledRandomVariable
- Computes the coefficient beta=Cov(X,Y)/Var(X), where Y is the
control variate of (
this) X conditioned on
information available at time t .
- betaCoefficient(int) -
Method in class Statistics.ControlledRandomVariable
- Same as
ControlledRandomVariable.betaCoefficient(int,int) with no information to
condition on.
- bfgsUpdate() -
Method in class Optimizers.BFGS
- The bfgs update of the approximate inverse Hessian.
- blackScholesFunction(double, double, double) -
Static method in class Statistics.FinMath
- Computes the function QN(h_+)-kN(h_-).
- blas -
Static variable in class LinAlg.ColtMatrix
- cern.colt.matrix.linalg.SeqBlas (non mulTithreaded) Blas object.
- blas -
Static variable in class LinAlg.ColtSparseMatrix
- cern.colt.matrix.linalg.SeqBlas (non mulTithreaded) Blas object.
- blas -
Static variable in class LinAlg.ColtVector
- cern.colt.matrix.linalg.SeqBlas (non mulTithreaded) Blas object.
- blas -
Static variable in class LinAlg.ExtendedColtMatrix
- cern.colt.matrix.linalg.SeqBlas (non mulTithreaded) Blas object.
- blas -
Static variable in class LinAlg.ExtendedColtVector
- cern.colt.matrix.linalg.SeqBlas (non mulTithreaded) Blas object.
- boundaryProjection(double[]) -
Static method in class Examples.Probability.DirichletProblem
- projects the point (u,v) radially on the unit circle
- bsDiscountedCallPrice(double, double, double, double, double) -
Static method in class Statistics.FinMath
- Discounted Black-Scholes call price (note that current time may
* not be zero).
- bsDiscountedPutPrice(double, double, double, double, double) -
Static method in class Statistics.FinMath
- Discounted Black-Scholes put price (note that current time may
* not be zero).
CS_FactorLoading).
CS_FactorLoading
in the jUnit testing framework.jUnit test suite for the class Calibrator.hasAnalyticPrice=false.
CRF(p,q,K1,K2) is simply
a call on the ReverseFloater RF(p,q,K1,K2) with zero
strike expiring at time T_p.L_j needed for j>=p and until time
T_p.
Option.minimumVarianceDelta(int, int, int) yield compared to instantaneous
analytic deltas.[T_p,T_q] with strike rate kappa
implemented as the sum of individual caplets.L_j needed for j>=p and until time
min(T_q,T_{n-1}) (for forward transporting the payoff).
cplt([T_i,T_{i+1}],k) pays off
h=delta_i*(L_i(T_i)-k)^+, where k is the
strike rate.L_j needed for j>=i and until time
min(T_{i+1},T_{n-1}) (for forward transporting the payoff
from time T_{i+1}).
jUnit test suite for the class Caplet.cern.colt.matrix.impl.DenseDoubleMatrix2D and
cern.colt.matrix.linalg.(Blas,Algebra,CholeskyDecomposition).junit test suite for the ColtMatrix class.cern.colt.matrix.impl.SparseDoubleMatrix2D matrices.Asset.super.newWienerIncrements is overriden and no
longr uses sign changes.
x, x+delta*e_j.
RandomVariable by the use of control variates.double[] x defined on the unit
cube Q=(0,1)^dim and intended to be used as an integrand
to check the effectiveness of the various low discrepancy sequences in
QMC integration.CS_FactorLoading to caplet and swaption
prices.
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
Calibrator.capletSigma computed from price
c, strike kappa and time
T_i by inversion the Black caplet formula.
capletSigma=sigma_i*sqrt(T_i), where
sigma_i is the annual caplet price implied volatility
of Libor L_i.
Sigma=sigma*sqrt(T_i), where
sigma is the annualized volatility
of the caplet cplt([T_i,T_{i+1}],kappa).
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
this) conditioned on information
available at time t.
this.
this.
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
this.
this.
(Cov(X_i,X_j))_{i,j=0}^{dim-1}
computed from a sample of size N and
conditioned on information available at time t.
EmpiricalDistribution of X (this)
conditioned on information available at time
t.
ControlledRandomVariable.conditionalExpectation(int,int) but with
computational progress reported to progress bar.
ControlledRandomVariable.conditionalExpectation(int,int) but sample size
increased until desired precision is reached with desired confidence.
ControlledRandomVariable.conditionalExpectation(int,double,double) but samples
come in groups of dependent samples of size sampleGroupSize.
RandomVariable.conditionalExpectation(int,int) but with
computational progress reported to a progress bar.
RandomVariable.conditionalExpectation(int,int)
but sample size increased until desired precision is reached
with desired confidence.
RandomVariable.conditionalExpectation(int,double,double)
but samples come in groups of dependent samples of size sampleGroupSize.
RandomVector.conditionalExpectation(int,int) with computational
progress reported to a progress bar.
RandomVariable.displayConditionalHistogram(int,int,int,boolean,String,String)
but no title or axis labels.
this) conditioned on information
available at time.
RandomVariable.conditionalMeanAndStandardDeviation(int, int)
but with computational progress reported to a progress bar.
RandomVariable.conditionalMeanAndStandardDeviation(int,int,int)
but with computational progress reported to a progress bar.
RandomVector.conditionalMeanAndStandardDeviation(int,int) with computational
progress reported to a progress bar.
RandomVector.conditionalMeanAndStandardDeviation(int,int,int)
computational progress is reported to a progress bar.
t.
t.
t.
t.
t.
t.
cap([T_p,T_q],kappa)
forward payoff and so its mean is the cap forward price.
T_n-forward price at time
discrete t (continuous time T_t).
this).
h(t)>g(t,Q(t)), where
g(t,x)=beta(x/beta)^alpha with alpha,beta
depending on t and increasing to one, respectively
decreasing to zero as the end of the swap approaches.
b into a.
RandomVector.conditionalCorrelation(int,int,int,int).
rho_ij as a ColtMatrix.
rho_ij as a ColtMatrix.
rho_ij as a ColtMatrix.
n by n matrix of instantaneous log-Libor
correlations (rho_ij)_{0<=i,j<n}.
this)
random variable X conditioned on information
available at time t and computed from a sample of size N.
ControlledRandomVariable.correlationWithControlVariate(int,int)
but no information to condition on.
RandomVector.conditionalCovariance(int,int,int,int).
max{ N, empiricalDist.nSamples }.
(B_p(T_p)-B_q(T_p))/B_n(T_p)(1-B_q(T_p))/B_n(T_p).
L_i(T_i)*B_{i+1}(T_i)/B_n(T_i), j=p,...,q-1.
L_i(T_i)*B_{i+1}(T_i)/B_n(T_i).
delta_jL_j(T_j)*B_{j+1}(T_j)/B_n(T_j)=
(B_j(T_j)-B_{j+1}(T_j))/B_n(T_j)
for j=p,p+1,...,q-1.
(B_p(T)-B_q(T))/B_n(T), T=T_tau.
cap([T_p,T_q],kappa).
h(rho_t) based on a given
exercise strategy rho=(rho_t) computed from the current path
of the underlying at time t, that is, the option has not been
exercised before time t.
h(rho_t) based on a given
exercise strategy rho=(rho_t) computed from the current path
of the underlying at time t, that is, the option has not been
exercised before time t.
t computed from
the current Libor path.
h(rho_t) based on a given
exercise strategy rho=(rho_t) computed from the current path
of the Libor process at time t, that is, the option has not
been exercised before time t.
T_p is simply the price
transported forward to time
T_n, value in current Libor path.
T_n.
T_{i+1} to time T_n.
LP.X of true Libors only.
T_n, value in current Libor path.
delta_k*(L_k(T_k)-kappa)=X_k(T_k)-delta_k*kappa
at times T_{k+1}, k=p,p+1,...,q-1 transported forward
and aggregated at the horizon T_n, value computed
from current Libor path.
T_tau from current Libor path transported
forward from time T_tau to time T_n.
T_n, value in current Libor path.
T_i transported forward to time
T_n, value in current Libor path.
result[0]==(U_{t+1}-U_t)^+ in the definition of
the random variable K from AmericanOptions.tex (3.15)
and result[1]==m_{t+1}
from the current path of the underlying computed up to time
rho_t.
t.
Hedge using a delta hedge (defined in the package
TradingStrategies)
as the trading strategy hedging the option payoff.Asset.x, x+delta*e_j.
FinMath.N(double).
dim from the row encoded from in the array gMR
and then reencodes the columns for use with the Gray counter
point generation algorithm (bottom up encoding).
dim from the row encoded in the array gMR
and then reencodes the columns for use with the Gray counter
point generation algorithm (bottom up encoding).
this.
this.
this.
this.
delta[j]=delta_j of accrual periods.
t.
n of Libors including L_0.
C(t).
TradingStrategy.newDiscountedGainsAndNumberOfTrades() as a random
vector.
VectorStrategy.newDiscountedGainsAndNumberOfTrades() as a random
vector.
RandomVariable.displayConditionalHistogram(int,int,int,,boolean,String,String)
but no title or axis labels.
RandomVariable.displayConditionalHistogram(int,int,int,boolean,String,String,String,int)
but no title or axis labels.
Z=X/Y, where X is the
current random variable this.
EP_FactorLoading).
sigma_j(t)=c_jg(1-t/T_j) with g(t)=1+Ah(t)
where h(t)=t(1-t) and correlations rho_ij=b_i/b_j,
for i<=j, with b_i=exp(beta*i^alpha).Examples.ArrayExamples.ControlVariatesExamples.HedgingExamples.LiborExamples.PathsExamples.pricingExamples.ProbabilityExamples.QuasiMonteCarloExamples.Tradingcern.colt.matrix.impl.DenseDoubleMatrix2D and
cern.colt.matrix.linalg.(Blas,Algebra,CholeskyDecomposition).this).
x at time t along training path
i, false otherwise.
x at time t along training path
i , false otherwise.
x at time t along training path
i, false otherwise.
ControlledRandomVariable.expectation(int) but with progress reported to a
progress bar.
ControlledRandomVariable.conditionalExpectation(int,double,double)
but no information to condition on.
ControlledRandomVariable.conditionalExpectation(int,double,double,int)
but no information to condition on.
RandomVariable.expectation(int) but with computational progress
reported to a progress bar.
RandomVariable.conditionalExpectation(int,double,double)
but no information to condition on.
RandomVariable.conditionalExpectation(int,double,double,int)
but no information to condition on.
RandomVector.conditionalExpectation(int,int).
RandomVector.conditionalExpectation(int,int,int,JProgressBar).
A=this assumed square, computes the polyonomial
exp(A,k)=I+A+A^2/2!+...+A^{k-1}/(k-1)!,
Obviously this is the exponential of A only if
A is nilpotent with A^k=0.
F_1(t) , see VarTemp.tex.
F_1(t) , see VarTemp.tex.
F_3(t) , see VarTemp.tex.
nu_i(s)
in the form of the Libor volatilities sigma_i(t) and
correlations rho_ij.jas.hist.Rebinnable1DHistogramData
defined in the JAS
library.fileNmae in current directory.
javax.swing.JFrame with the ability to save the graphics
in its only component to a file in various file formats.FactorLoading of the Libor process
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
this)
stored in the unconditional empirical distribution of X to N.
B_i(t)/B_n(t).
1/B(T_i,T_n)=1/B(n,i).
1/B(T_m,T_n)=1/B(n,m).
f(x)=x^alpha*exp(-x/beta)/Gamma(alpha)beta^alpha
.
GraphicsBermudanSwaption.Q(int) to approximate the true
continuation value CV(t).
AmericanBasketOption.Q(int, int) to approximate the true
continuation value CV(t).
AmericanBlackScholesPut.Q(int) to approximate the true
continuation value CV(t).
h as
h(u)=g(m*u-[m*u]).
cern.jet.random.Beta Beta distribution.
cern.jet.random.Binomial Binomial
distribution.
cern.jet.random.ChiSquare chisquare
distribution.
this) X
conditioned on information available at
time t and computes the corresponding control variate cv.
n of forward Libors including L_0.
cern.jet.random.Exponential
Exponential distribution.
cern.jet.random.Gamma Gamma distribution.
cern.jet.random.HyperGeometric
Hypergeometric distribution.
cern.jet.random.NegativeBinomial
NegativeBinomial distribution.
cern.jet.random.Poisson distribution.
rho[][].
rho[][].
rho[][].
Tc of continuous Libor reset times
Tc[j]=T_j.
t.
this) conditioned on information
available at time t.
super.getValue(int) from
getControlledValue()
True if the
empirical distribution has been initilized
(filled with samples) false else.
True if analytic formula for the unconditional mean
implemented false else.
True if an analytic formula for the unconditional mean is
implemented false else.
True if analytic formula for the unconditional mean
implemented false else.
True if and analytic formula for the option price
is implemented, false otherwise.
True if an analytic formula for the unconditional variance
is implemented false else.
True if analytic formula for the unconditional mean
implemented false else.
True if analytic formula for the conditional mean is
implemented false else.
True if analytic formula for the unconditional mean
implemented false else.
True if an analytic formula for the conditional variance
is implemented false else.
this)
* process X, path[t]=X(t*dt).
this).
sigma(t)*sqrt(dt), where sigma(t) is the volatility of
the asset.
sigma(t)*sqrt(dt), where sigma(t) is the volatility of
the asset.
this is a path functional.
Optimizer.f(double[]) at the point
x by finite differencing against n other points
(x+h*e_j).
Optimizer.f(double[]) at the point
x by central finite differencing from 2n points
(x+-h*e_j).
H(t).
Hedgingh=h(u) of one variable u defining
this function f as
f(x)=h(x_1)*h(x_2)*....*h(x_d), where d=dim.
- hIntegral() -
Method in class QuasiRandom.Intgrnd
- The integral of h over (0,1).
- hIntegral() -
Method in class QuasiRandom.SeparableCubeFunction
- Integral of
h over (0,1).
- hasAnalyticPrice() -
Method in class Options.Option
True if and analytic formula for the option price
* is implemented, false otherwise.
- hedgeErrorVarianceReduction(int) -
Method in class Options.BlackScholesCall
-
Computes the reduction in the variance of the hedge error over the
first hedge interval [0,dt] which the variance minimizing delta provides
compared to the analytic delta as a percentage of the minimal variance.
- hedgeMeanAndStandardDeviation(int) -
Method in class Hedging.Hedge
- Computes mean (return_value[0]) and standard deviation (return_value[1])
of the profit and loss from hedging a short position in the option
(on one share of the underlying).
- hedgeMeanAndStandardDeviation(int, int, JProgressBar) -
Method in class Hedging.Hedge
- Same as
Hedge.hedgeMeanAndStandardDeviation(int) with progress
reported to progress bar.
- hedgeMeanAndStandardDeviation(int) -
Method in class Hedging.VectorHedge
- Computes mean (return_value[0]) and standard deviation (return_value[1])
of the profit and loss from hedging a short position in the option
(on one share of the underlying).
- hedgeMeanAndStandardDeviation(int, int, JProgressBar) -
Method in class Hedging.VectorHedge
- Same as
VectorHedge.hedgeMeanAndStandardDeviation(int) with progress
reported to progress bar.
- hedgeStatistics() -
Method in class Hedging.Hedge
- The
Hedge.newHedgeStatistics() as a random vector.
- hedgeStatistics() -
Method in class Hedging.VectorHedge
- The
VectorHedge.newHedgeStatistics() as a random vector.
- histogram() -
Method in class Statistics.FixedBinDataSource
- Returns a histogram of type
jas.hist.JASHist of the
data set.
- histogram(int, int, boolean, String, String) -
Method in class Statistics.RandomVariable
- Unconditional histogram.
- histogram(int, int, boolean) -
Method in class Statistics.RandomVariable
- Unconditional histogram.
IOf(x)=h(x_1)*h(x_2)*...*h(x_d), where d=dimension,
h(u)=g(m*u-[m*u]) and g=g(u) is a function of
one variable u\in(0,1) and [t] denotes the largest integer
not greater than t as usual.Intgrnd with g(u)=u-0.5.Intgrnd with g(u)=exp(N_Inverse(u)).Intgrnd with g(u)=sin^2(2pi*u).Intgrnd with g(u)=(n+1)*u^n.Intgrnd with g(u)=4-12(u-0.5)^2.Intgrnd with g(u)=3*I_[1/3,2/3](u)
(indicator function).Id_n.
empiricalDist)
of X (this) with N samples.
l[j]=L_j(0), j
- initialTermStructure() -
Method in class Libor.LiborProcess.LiborProcess
- The array
l[j]=L_j(0) of initial Libors.
- initialize(int, ColtMatrix) -
Method in class ArrayClasses.LTRMatrixArray
- Set entries
data[t][i][j]=C.getQuick(i,j) where
C is a ColtMatrix.
- initialize(DoubleFunction) -
Method in class ArrayClasses.LTRMatrixArray
- Set entries
data[t][i][j]=f(t,i,j) where
f is the function object passed as a parameter.
- initialize(ColtMatrix) -
Method in class ArrayClasses.LowerTriangularArray
- Set entries
data[i][j]=C.getQuick(i,j) where
C is a ColtMatrix.
- initialize(DoubleFunction) -
Method in class ArrayClasses.LowerTriangularArray
- Set entries
data[i][j]=f(i,j) where
f is the function object passed as a parameter.
- initialize(int, ColtMatrix) -
Method in class ArrayClasses.UTRMatrixArray
- Set entries
data[t][i][j]=C.getQuick(i,j) where
C is a ColtMatrix.
- initialize(DoubleFunction) -
Method in class ArrayClasses.UTRMatrixArray
- Set entries
data[t][i][j]=f(t,i,j) where
f is the function object passed as a parameter.
- initialize(ColtMatrix) -
Method in class ArrayClasses.UpperTriangularArray
- Set entries
data[i][j]=C.getQuick(i,j) where
C is a ColtMatrix.
- initialize(DoubleFunction) -
Method in class ArrayClasses.UpperTriangularArray
- Set entries
data[i][j]=f(i,j) where
f is the function object passed as a parameter.
- instantaneousReturnsVolatility(int, int) -
Method in class Market.DeterministicVolBasket
- The volatility $\sigma_i(t)$ of asset i.
- integral() -
Method in class QuasiRandom.CubeFunction
- The integral of
this over the unit cube
Q=(0,1)^dim.
- integral() -
Method in class QuasiRandom.SeparableCubeFunction
- The integral of
this over the unit cube Q=(0,1)^dim.
- integralSgiSgj(double, double, int, int) -
Method in class Market.DeterministicVolBasket
- The integral $\int_a^b\sigma_i(s)\sigma_j(s)ds$ (TeX notation,
sigma_i(t) the volatility of asset S_i).
- integralSigmaSquare(double, double) -
Method in class Market.DeterministicVolAsset
- The quadratic variation < log(S)>_a^bintegral_a^b sigma^2(u)du
variation of the return process log(S) over the time
interval [a,b].
- integral_g_squared(double) -
Method in class Libor.LiborProcess.Calibrator
integral_0^T g(u)^2du, where g(u) is the
function defining the volatilities
sigma_j(t)=c_jg(1-t/T_j) in the CS_FactorLoading.
- integral_sgi_sgj_rhoij(int, int, double, double) -
Method in class Libor.LiborProcess.CS_FactorLoading
- The integral
integral_t^T sigma_i(s)sigma_j(s)rho_ijds
=<log(L_i),log(L_j)>_t^T
neeeded for the distribution of time step increments.
- integral_sgi_sgj_rhoij(int, int, double, double) -
Method in class Libor.LiborProcess.EP_FactorLoading
- The integral
<log(L_i),log(L_j)>_t^T=
int_t^T sigma_i(s)sigma_j(s)rho_ijds
neeeded for the distribution of time step increments.
- integral_sgi_sgj_rhoij(int, int, double, double) -
Method in class Libor.LiborProcess.FactorLoading
- The integral
integral_t^T sigma_i(s)sigma_j(s)rho_ijds=
<log(L_i),log(L_j)>_t^T
neeeded for the distribution of time step increments.
- integral_sgi_sgj_rhoij(int, int, double, double) -
Method in class Libor.LiborProcess.JR_FactorLoading
- The integral
integral_t^T sigma_i(s)sigma_j(s)rho_ijds
=<log(L_i),log(L_j)>_t^T
neeeded for the distribution of time step increments.
- inverse() -
Method in class LinAlg.ColtMatrix
- Inverse of
A=this if A is square, pseudoinverse otherwise.
- inverse() -
Method in class LinAlg.ExtendedColtMatrix
- Inverse of
A=this if A is square, pseudoinverse otherwise.
- isDiagonal() -
Method in class LinAlg.ColtMatrix
-
- isDiagonal() -
Method in class LinAlg.ExtendedColtMatrix
-
- isEqual(ColtMatrix) -
Method in class LinAlg.ColtMatrix
- Test for equality of dimension and entry by entry equality.
- isEqual(ColtVector) -
Method in class LinAlg.ColtVector
- Test for equality of dimension and component by component equality.
- isInDomain(double[]) -
Method in class Optimizers.ConstrainedDownhillSimplex
- SEARCH DOMAIN
- isLowerTriangular() -
Method in class LinAlg.ColtMatrix
-
- isLowerTriangular() -
Method in class LinAlg.ExtendedColtMatrix
-
- isMember(double) -
Method in interface Processes.Region_1D
- The number x is either in the region or not.
- isMember(double[]) -
Method in interface Processes.Region_nD
- The vector x is either in the region or it's not,
* no checking of dimension.
- isRebinnable() -
Method in class Statistics.FixedBinDataSource
false, communicates to the JAS plot widget that
current number of bins must be used.
- isRebinnable() -
Method in class Statistics.PointDataSource
-
- isSymmetric() -
Method in class LinAlg.ColtMatrix
-
- isSymmetric() -
Method in class LinAlg.ExtendedColtMatrix
-
- isTriangular() -
Method in class LinAlg.ColtMatrix
-
- isTriangular() -
Method in class LinAlg.ExtendedColtMatrix
-
- isTriggered(int, int) -
Method in class Libor.LiborDerivatives.CvxTrigger
- THE TRIGGER CONDITION
- isTriggered(int, int) -
Method in class Libor.LiborDerivatives.PjTrigger
- True if exercise is triggered at time
t false otherwise.
- isTriggered(int, int) -
Method in class Options.AmericanPutCvxTrigger
- THE TRIGGER CONDITION
- isTriggered(int, int) -
Method in class Triggers.NullTrigger
- Event is triggered only as the time horizon is hit.
- isTriggered(int, int) -
Method in class Triggers.Trigger
- Returns true if the event is triggered at time s with reference
to time t < s.
- isTriggered(int, int) -
Method in class Triggers.TriggerAtEachTimeStep
- Triggers at each integer s.
- isTriggered(int, int) -
Method in class Triggers.TriggerAtPercentChange
- The trigger is a q percent change in the discounted asset price
since time t.
- isTriggered(int, int) -
Method in class Triggers.TriggerAtPercentDecline
- The trigger is a q percent decline in the discounted asset price
since time t.
- isTriggered(int, int) -
Method in class Triggers.TriggerAtPercentIncrease
- The trigger is a q percent increase in the discounted asset price
since time t.
- isTriggered(int, int) -
Method in class Triggers.TriggerPeriodic
- Triggers at equal time intervals so as to get as close to m events
as possible.
- isUpperTriangular() -
Method in class LinAlg.ColtMatrix
-
- isUpperTriangular() -
Method in class LinAlg.ExtendedColtMatrix
-
JR_FactorLoading).
CS_FactorLoading
in the jUnit testing framework.ConstantVolatilityAsset with jumps.CallHedgeStatistics except that the underlying asset
is a JumpAsset.(E_t[U_{t+1}-U_t])^+ in the definition
of the random variable K from AmericanOptions.tex (3.15).
L_j(t), value in current path.
L_j(T_j) as a random variable.
L^0_j(t), value in current path.
L^0_j(T_j) as a random variable.
L^1_j(t), value in current path.
L^1_j(T_j) as a random variable.
l[j]=L_j(0)
with a FactorLoading object and hence provides everything
to set up a Libor process.l[i]=L_i(0) and FactorLoading fl.
A of n-1 lower triangular matrices
(arrays).new.
LiborDerivativesLiborProcessn=100 with time steps delta_j=0.25.X0,X1, see LiborProcess.ps).LMM_Parameters parameter
object.
LiborProcess
in the jUnit testing framework.RandomVector of Libors
U=(X_m(T_m),X_{m+1}(T_m),...,X_{n-1}(T_m)) as seen from time
t=0, ie.X0 or
X1 (see LiborProcess.ps).
LinAlgL0_j(T_j) and L1_j(T_j) approximate true Libor
L_j(T_j).(a_ij)_{0<=j<=i
stored as straightforward ragged java array.- LowerTriangularArray(int) -
Constructor for class ArrayClasses.LowerTriangularArray
- Memory allocation, all entries zero.
- leftTimesEquals(ColtMatrix, boolean) -
Method in class LinAlg.ColtMatrix
- Implements the operation
this=A*this.
- leftTimesEquals(ColtMatrix) -
Method in class LinAlg.ColtMatrix
- Implements the operation
this=A*this.
- leftTimesEquals(ExtendedColtMatrix, boolean) -
Method in class LinAlg.ExtendedColtMatrix
- Implements the operation
this=A*this.
- leftTimesEquals(ExtendedColtMatrix) -
Method in class LinAlg.ExtendedColtMatrix
- Implements the operation
this=A*this.
- liborProcessTestSuite() -
Static method in class Libor.LiborProcess.LiborProcessTest
- Returns the test suite object which is then
run
in one of the test suite runners juint.textui.TestRunner or
junit.swingui.TestRunner.
- linAlg -
Static variable in class LinAlg.ColtMatrix
- cern.colt.matrix.linalg.Algebra object with default tolerances.
- linAlg -
Static variable in class LinAlg.ColtSparseMatrix
- cern.colt.matrix.linalg.Algebra object with default tolerances.
- linAlg -
Static variable in class LinAlg.ColtVector
- cern.colt.matrix.linalg.Algebra object with default tolerances.
- linAlg -
Static variable in class LinAlg.ExtendedColtMatrix
- cern.colt.matrix.linalg.Algebra object with default tolerances.
- linAlg -
Static variable in class LinAlg.ExtendedColtVector
- cern.colt.matrix.linalg.Algebra object with default tolerances.
- linearSystemSolution(ColtMatrix) -
Method in class LinAlg.ColtMatrix
- Returns the solution matrix X of the linear equation
this*X=B.
- linearSystemSolution(ColtVector) -
Method in class LinAlg.ColtMatrix
- Returns the solution vector x
of the linear equation this*x=y.
- linearSystemSolution(ColtSparseMatrix, ColtVector) -
Static method in class LinAlg.ColtSparseMatrix
- Returns the
ColtVector solution vector x
of the linear equation Ax=y.
- linearSystemSolution(ExtendedColtMatrix) -
Method in class LinAlg.ExtendedColtMatrix
- Returns the solution matrix X of the linear equation
this*X=B.
- linearSystemSolution(ExtendedColtVector) -
Method in class LinAlg.ExtendedColtMatrix
- Returns the solution vector x
of the linear equation this*x=y.
- linearSystemSolution(DoubleMatrix2D, DoubleMatrix1D) -
Static method in class Statistics.FinMath
- Returns cern.colt.matrix.impl.DenseDoubleMatrix1D solution vector x
* of the linear equation Ax=y.
- logCovariationCholeskyRoot(int, int, double, double) -
Method in class Libor.LiborProcess.Calibrator
- The Cholesky root of
FactorLoading.logCovariationMatrix(int,int,double,double).
- logCovariationCholeskyRoot(int) -
Method in class Libor.LiborProcess.FactorLoading
- The Cholesky root of the
FactorLoading.logCovariationMatrix(int)
This matrix is needed to drive the time step t->t+1
of the Libors (L_{t+1},...,L_{n-1}).
- logCovariationCholeskyRoot(int, int, double, double) -
Method in class Libor.LiborProcess.FactorLoading
- The Cholesky root of
FactorLoading.logCovariationMatrix(int,int,double,double).
- logCovariationCholeskyRoot(int, int, double, double) -
Method in class Libor.LiborProcess.LiborProcess
- The Cholesky root of
LiborProcess.logCovariationMatrix(int,int,double,double).
- logCovariationMatrix(int) -
Method in class Libor.LiborProcess.FactorLoading
- The matrix of covariations
<log(L_i),log(L_j)>_{T_t}^{T_{t+1}}
- logCovariationMatrix(int, int, double, double) -
Method in class Libor.LiborProcess.FactorLoading
- The matrix of log-Libor covariations
<log(L_i),log(L_j)>_t^T on the interval
[t,T]
- logCovariationMatrix(int, int, double, double) -
Method in class Libor.LiborProcess.LiborProcess
- The log-Libor covariation-matrix
- logL(int) -
Method in class Libor.LiborProcess.LiborProcess
- Log-Libor
log(L_j(T_j)) as a random variable.
- logL0(int) -
Method in class Libor.LiborProcess.LiborProcess
- Gaussian log-Libor
log(L^0_j(T_j)) as a random variable.
- logL1(int) -
Method in class Libor.LiborProcess.LiborProcess
- Gaussian log-Libor
log(L^1_j(T_j)) as a random variable.
- lognormalForwardPayoff() -
Method in class Libor.LiborDerivatives.LiborDerivative
- The
LiborDerivative.lognormalForwardPayoff() as a random variable based
on the log-normal Libor vector this.LV as a proxy for
the vector of true Libors.
- lognormalForwardPayoffSample() -
Method in class Libor.LiborDerivatives.LiborDerivative
- The forward transported payoff seen from time
t=0 and
computed from a new sample of the Libor vector this.LV
instead of true Libors derived from paths of the underlying
LiborProcess.
- lognormalForwardPayoffSample() -
Method in class Libor.LiborDerivatives.Swaption
- The forward transported payoff (as seen from time
t=0)
computed from a new sample of the LiborVector object
U=(X^0_tau(T_tau),...,X^0_{n-1}(T_tau)), a log-normal
approximation to the vector of true Libors
U=(X_tau(T_tau),...,X_{n-1}(T_tau)).
- lognormalForwardPayoffSample() -
Method in class Libor.LiborDerivatives.ZeroCouponBond
- The forward transported payoff (as seen from time
t=0)
computed from a new sample of the LiborVector object
U=(X^0_i(T_i),...,X^0_{n-1}(T_i)), a log-normal
approximating to the vector of true Libors
U=(X_i(T_i),...,X_{n-1}(T_i)).
- lognormalMonteCarloForwardPrice(int) -
Method in class Libor.LiborDerivatives.LiborDerivative
- The value of the time
T_n-forward price at time
discrete t=0 (continuous time T_t)
computed by direct simulation of the approximating log-normal
Libor vector this.LV instead of true Libor paths
(speed).
Marketsuper.this
* with dt=1.
edu.cornell.lassp.houle.RngPack.RandomElement based on
uniform random numbers generated by a Mersenne Twister.L_75
and the corresponding path of driftless Libor code>L^0_75
whenever the window is clicked.
- main(String[]) -
Static method in class Examples.Libor.LiborPaths
- Opens the window and displays a new path of Libor
L_75
and the corresponding path of driftless Libor code>L^0_75
whenever the window is clicked.
- main(String[]) -
Static method in class Examples.Libor.LogNormalLibor
-
- main(String[]) -
Static method in class Examples.Libor.PathTiming
-
- main(String[]) -
Static method in class Examples.Miscellaneous.RandomNumberTiming
-
- main(String[]) -
Static method in class Examples.Paths.CallDeltaPaths
- Displays a new path of the delta of a one year call whenever
the window is clicked.
- main(String[]) -
Static method in class Examples.Paths.CallThetaPaths
- Displays a new path of the theta of a one year call whenever
the window is clicked.
- main(String[]) -
Static method in class Examples.Paths.JumpAssetPaths
- Displays a new path of the delta of a one year call whenever
the window is clicked.
- main(String[]) -
Static method in class Examples.Pricing.AmericanBasketPrice
-
- main(String[]) -
Static method in class Examples.Pricing.CallPriceAndDeltas
-
- main(String[]) -
Static method in class Examples.Pricing.CallPriceQMC
-
- main(String[]) -
Static method in class Examples.Pricing.MC_Asset_Test
-
- main(String[]) -
Static method in class Examples.Pricing.QMCversusMC_1
-
- main(String[]) -
Static method in class Examples.Pricing.QMCversusMC_2
-
- main(String[]) -
Static method in class Examples.Pricing.SwaptionPrice
-
- main(String[]) -
Static method in class Examples.Probability.DirichletDemo
- * MAIN
*
- main(String[]) -
Static method in class Examples.Probability.DirichletProblem
-
- main(String[]) -
Static method in class Examples.Probability.EmpiricalHistogram
-
- main(String[]) -
Static method in class Examples.Probability.ExpectationTest
- MAIN
- main(String[]) -
Static method in class Examples.Probability.GamblersFortune
-
- main(String[]) -
Static method in class Examples.Probability.GamblersFortune_1
-
- main(String[]) -
Static method in class Examples.Probability.Insurance
-
- main(String[]) -
Static method in class Examples.Probability.OptionalSamplingTest
-
- main(String[]) -
Static method in class Examples.Probability.PathBranchDemo
- SHOW YOURSELF
- main(String[]) -
Static method in class Examples.Probability.PathFunctionalHistogram
- Display the histogram of the functional (
this)
over 200,000 paths using 100 bins.
- main(String[]) -
Static method in class Examples.Probability.Urns
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.GrayCodeCounter
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.L2Discrepancy
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.L2DiscrepancyGraph
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.L2NX
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.LowDiscrepancyPoints
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.Matrices
-
- main(String[]) -
Static method in class Examples.QuasiMonteCarlo.QmcIntegration
-
- main(String[]) -
Static method in class Examples.Trading.AverageDown
-
- main(String[]) -
Static method in class Examples.Trading.BuyAndHold
-
- main(String[]) -
Static method in class Examples.Trading.DollarCostAveraging
-
- main(String[]) -
Static method in class Examples.Trading.DoubleOrNothing
-
- main(String[]) -
Static method in class Examples.Trading.GainsFromTrading
-
- main(String[]) -
Static method in class Examples.Trading.ReturnsHistogram
-
- main(String[]) -
Static method in class Examples.Trading.TradingGainsHistogram
-
- main(String[]) -
Static method in class Graphics.JGraph
- Test program displaying the graph of three functions f(x)=x^2,
g(x)=5-x^2, h(x)=2+x^2/2 and saving the graph as the file
testGraph.jpeg.
- main(String[]) -
Static method in class Graphics.PathFrame
- Test program.
- main(String[]) -
Static method in class Graphics.PointFrame
- Test program.
- main(String[]) -
Static method in class Hedging.CallHedgeStatisticsGraphs
- Test program testing graphFunctionOfStrikeAllDeltas on analytic deltas.
- main(String[]) -
Static method in class Hedging.DeltaHedge
- Test program.
- main(String[]) -
Static method in class Hedging.OptionHedge
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.BSWPNTest
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.BermudanExerciseBoundary
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.BermudanSwaption
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.CallableReverseFloater
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.Cap
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.Caplet
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.CapletTest
- run the tests in a Swing GUI
- main(String[]) -
Static method in class Libor.LiborDerivatives.CvxTrigger
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.PjTrigger
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.ReverseFloater
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.Swap
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.Swaption
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.TriggerSwap
- Test program.
- main(String[]) -
Static method in class Libor.LiborDerivatives.ZeroCouponBond
- Test program.
- main(String[]) -
Static method in class Libor.LiborProcess.CS_FactorLoading
- Small test program.
- main(String[]) -
Static method in class Libor.LiborProcess.CS_FactorLoadingTest
- run the tests in a Swing GUI
- main(String[]) -
Static method in class Libor.LiborProcess.Calibrator
- Calibration test,
calibrates a
CS_FactorLoading to a set of data in dimension
n=15.
- main(String[]) -
Static method in class Libor.LiborProcess.CalibratorTest
- run the tests in a Swing GUI
- main(String[]) -
Static method in class Libor.LiborProcess.EP_FactorLoading
- Small test program.
- main(String[]) -
Static method in class Libor.LiborProcess.JR_FactorLoading
- Small test program.
- main(String[]) -
Static method in class Libor.LiborProcess.JR_FactorLoadingTest
- run the tests in a Swing GUI
- main(String[]) -
Static method in class Libor.LiborProcess.LiborProcess
- Test program.
- main(String[]) -
Static method in class Libor.LiborProcess.LiborProcessTest
- Run the tests in a text UI.
- main(String[]) -
Static method in class Libor.LiborProcess.SyntheticData
- Test program.
- main(String[]) -
Static method in class LinAlg.ColtMatrix
- Times the computation of nilpotent matrix exponentials.
- main(String[]) -
Static method in class LinAlg.ColtMatrixTest
- run the tests in a Swing GUI
- main(String[]) -
Static method in class LinAlg.ColtSparseMatrix
- TEST PROGRAM
- main(String[]) -
Static method in class LinAlg.ColtVector
- Run, output is self explanatory.
- main(String[]) -
Static method in class LinAlg.ExtendedColtMatrix
- TEST PROGRAM
- main(String[]) -
Static method in class LinAlg.ExtendedColtVector
- Output is selfexplanatory.
- main(String[]) -
Static method in class Optimizers.BFGS
- Test program.
- main(String[]) -
Static method in class Optimizers.ConstrainedDownhillSimplex
- Test program.
- main(String[]) -
Static method in class Optimizers.DownhillSimplex
- Test program.
- main(String[]) -
Static method in class Optimizers.LowDiscrepancySearch
- Test program.
- main(String[]) -
Static method in class Options.AmericanBlackScholesPut
- Test program
- main(String[]) -
Static method in class Options.BlackScholesCall
- Program tests quality of the analytic approximation for market
deltas accross a variety of strikes and times to maturity.
- main(String[]) -
Static method in class QuasiRandom.DigitalRandomSequence
- TEST PROGRAM
- main(String[]) -
Static method in class QuasiRandom.Encode
-
- main(String[]) -
Static method in class QuasiRandom.NX
- TEST PROGRAM
- main(String[]) -
Static method in class QuasiRandom.Sobol
- Small test program, allocates a Sobol generator and prints several
Sobol points.
- main(String[]) -
Static method in class RandomVariables.BetaVariable
- Allocates a
Beta(0.8,1.2) variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.BinomialVariable
- Allocates a Binomial variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.ChiSquareVariable
- Allocates a
ChiSquare(20) variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.ExponentialVariable
- Allocates a
Exponential(0.2) variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.GammaVariable
- Allocates a Gamma variable using mean and variance as the parameters
and compares the analytic mean and variance to Monte Carlo mean and
variance over a sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.HyperGeometricVariable
- Allocates a
HyperGeometric(1000,400,300) variable
and compares the analytic mean and variance to Monte Carlo mean and
variance over a sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.NegativeBinomialVariable
- Allocates a
NegativeBinomial(20,0.3) variable and compares
the analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.NormalVariable
- Allocates a
Normal(3,2) variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class RandomVariables.PoissonVariable
- Allocates a
Poisson(3) variable and compares the
analytic mean and variance to Monte Carlo mean and variance over a
sample of size 100,000.
- main(String[]) -
Static method in class Statistics.FinMath
- TEST PROGRAM
- main(String[]) -
Static method in class Statistics.FixedBinDataSource
- Test program.
- main(String[]) -
Static method in class Statistics.RandomVariable
- Test program.
- main(String[]) -
Static method in class Statistics.RandomVector
- Tests the expectation of a random vector
of dimension 3 where component j is the sum of j+1 independent
standard normal random variables.
- mainComputation() -
Method in class Examples.Hedging.DrawCHGraphs_1
-
- mainComputation() -
Method in class Examples.Hedging.DrawCHGraphs_2
-
- mainComputation() -
Method in class Examples.Pricing.CallPriceAndDeltas
-
- markovChain(int, double, double) -
Method in class Market.ConstantVolatilityAsset
- Approximation of the discounted asset price path as a stationary finite
* state Markov chain.
- mean() -
Static method in class Examples.Probability.ExpectationTest
-
- mean(int) -
Method in class Statistics.RandVariable
- Unconditional expectation computed from sample of size N.
- meanAndStandardDeviation(int) -
Method in class Statistics.RandomVariable
- Unconditional mean (return_value[0]) and standard deviation
(return_value[1]) computed from sample of size N.
- meanAndStandardDeviation(int, int) -
Method in class Statistics.RandomVariable
- Same as
RandomVariable.conditionalMeanAndStandardDeviation(int,int,int),
but no information to condition on.
- meanAndStandardDeviation(int, int, JProgressBar) -
Method in class Statistics.RandomVariable
- Same as
RandomVariable.meanAndStandardDeviation(int)
but with computational progress reported to a progress bar.
- meanAndStandardDeviation(int, int, int, JProgressBar) -
Method in class Statistics.RandomVariable
- Same as
(int,int,int,int,JProgressBar)
but no information to condition on.
- meanAndStandardDeviation(int) -
Method in class Statistics.RandomVector
- Unconditional version of
RandomVector.conditionalMeanAndStandardDeviation(int, int).
- meanAndStandardDeviation(int, int) -
Method in class Statistics.RandomVector
- Unconditional version of
RandomVector.conditionalMeanAndStandardDeviation(int,int,int).
- meanAndStandardDeviation(int, int, JProgressBar) -
Method in class Statistics.RandomVector
- Unconditional version of
RandomVector.conditionalMeanAndStandardDeviation(int,int,int,JProgressBar).
- meanAndStandardDeviation(int, int, int, JProgressBar) -
Method in class Statistics.RandomVector
- Unconditional version of
(int,int,int,int,JProgressBar).
- meanF1(int) -
Method in class Examples.Hedging.CallHedgeVariance
- Analytic mean of
F1.
- meanF2(int) -
Method in class Examples.Hedging.CallHedgeVariance
- Analytic mean of
F2.
- meanF3(int) -
Method in class Examples.Hedging.CallHedgeVariance
- Analytic mean of
F3.
- minimumVarianceDelta(int, int, int) -
Method in class Options.Option
- The minimum variance delta at time t (see file "HedgeWeights.ps")
* minimizing the squared hedge error over the next time step.
- minimumVarianceDelta(int, int, int, Trigger) -
Method in class Options.Option
- Same as
Option.minimumVarianceDelta(int,int,int) but the
* hedge is rebalanced when the next hedge trade is triggered and the
* squared error minimized over the stochastic time interval until
* the next hedge trade.
- minimumVarianceDelta(int, int, int) -
Method in class Options.PathIndependentOption
- The minimum variance delta at time t (see file "HedgeWeights.ps")
minimizing the squared hedge error over the next time step.
- minimumVarianceDelta(int, int, int, Trigger) -
Method in class Options.PathIndependentOption
- Same as
PathIndependentOption.minimumVarianceDelta(int,int,int) but the
hedge is rebalanced when the next hedge trade is triggered and the
squared error minimized over the stochastic time interval until
the next hedge trade.
- minimumVarianceDeltas(int, int, Trigger) -
Method in class Options.BasketOption
- Warning: not implemented yet.
- minus(RandomVariable) -
Method in class Statistics.RandomVariable
- Returns the variable
Z=X-Y, where X is the
current random variable this.
- monteCarloDelta(int, int) -
Method in class Options.Option
- The Monte Carlo delta at time t (see file "HedgeWeights.ps").
- monteCarloDeltas(int, int) -
Method in class Options.BasketOption
- The vector of Monte Carlo deltas of the underlying assets at time t
computed in the risk neutral probability.
- monteCarloDeltas(int, int) -
Method in class Options.PathIndptBasketOption
- The vector of Monte Carlo deltas of the underlying assets at time t
computed in the risk neutral probability.
- monteCarloForwardPrice(int, int, Trigger) -
Method in class Libor.LiborDerivatives.BermudanSwaption
- Monte Carlo price at time t dependent on a given exercise policy
computed as a conditional expectation
conditioned on information available at time t
and computed from a sample of nPath (branches of) the price path of the
underlying.
- monteCarloForwardPrice(int, Trigger) -
Method in class Libor.LiborDerivatives.BermudanSwaption
- Monte Carlo option price at time t=0.
- monteCarloForwardPrice(int, int) -
Method in class Libor.LiborDerivatives.LiborDerivative
- The value of the time
T_n-forward price at
discrete time t (continuous time T_t).
- mult(RandomVariable) -
Method in class Statistics.RandomVariable
- Returns the variable
Z=X*Y, where X is the
current random variable this.
- mult(double[], double[]) -
Static method in class Statistics.Vector
- Componentwise multiplication.
- myErrorFlags -
Variable in class com.skylit.io.EasyReader
-
- myErrorFlags -
Variable in class com.skylit.io.EasyWriter
-
- myFileName -
Variable in class com.skylit.io.EasyReader
-
- myFileName -
Variable in class com.skylit.io.EasyWriter
-
- myInFile -
Variable in class com.skylit.io.EasyReader
-
- myOutFile -
Variable in class com.skylit.io.EasyWriter
-
dim.
bLackScholesFunction(Q,k,Sigma)=y for
* Sigma> 0 using Newton's algorithm.
Option.discountedAnalyticPrice(int t) or
Option.analyticDelta(int t) is called
no analytic formula for price at time t of this option is implemented.NoAnalyticPriceException
without detailed message.
NoAnalyticPriceException
with the specified detailed message.
NoSolutionException without
detailed message.
NoSolutionException with the
specified detailed message.
ControlledRandomVariable.betaCoefficient(int, int).
LiborProcess.newPathSegment(int,int,int).
LiborProcess.newPathSegment(int,int,int,boolean,boolean,boolean).
t to the time
the s the trigger stop triggers a stop or
to time s=n-1 if no stop is triggered.
Z-array with a new set of independent standard
normal increments needed to evolve Libors from discrete time
t to discrete time T.
t=T_11.
t=T_11.
U=(X_m(T_m),X_{m+1}(T_m),...,X_{n-1}(T_m)) into the array
this.X.
t>=s where the event is triggered
with reference to t.
OptimizersHedge using a TradingStrategy defined by the
the abstract method OptionHedge.weight(int) trading in the underlying
to hedge the change in option value.Optionn=10,20,...,80 and plots the time taken for each run.jas.hist.JASHist histogram.PointFrame.PointFrame(String,int,int,int,int,double,double,
double,double,int,boolean) except that axes x=x0 and
y=y0 are drawn in color axesColor
ProcessesZ=X+Y, where X is the
current random variable this.
this+=alpha*A.
this+=A.
this+=z and returns
the resulting vector code>this.
this+=alpha*A.
this=beta*this+alpha*A*B.
this=beta*this+alpha*A*B.
this+=alpha*A*B.
this+=alpha*A*B.
this+=z.
this=beta*this+alpha*A*z.
this=beta*this+alpha*A*z.
this+=alpha*A*z.
this+=alpha*A*z.
this+=alpha*z.
(rho_ij) of instantaneous
log-Libor correlations for
h(t)>alpha*max{E_t(h(t+1),...,E_t(h(T))}.
h(t)>alpha*max{E_t(h(t+1),...,E_t(h(T))}.
rho=(rho_t).
h_t> Q(t)
Q(t).
Q(t)=max{ E_t(h_{t+1}), E_t(h_{t+2}),..., E_t(h_T) }
for the continuation value CV(t) computed from the current
Libor path.
Q(t)=max{ E_t(h_{t+1}), E_t(h_{t+2}),..., E_t(h_T) }
for the continuation value CV(t) computed from the current
path.
Q(t)=max{ E_t(h_{t+1}), E_t(h_{t+2}),..., E_t(h_T) }
for the continuation value CV(t) computed from the current
path.
Q(t)=max{ E_t(h_{t+1}), E_t(h_{t+2}),..., E_t(h_T) }
for the continuation value CV(t) computed from the current
path.
Intgrnds
over the unit cube Q=(0,1)^dim.QuasiRandomSFSMarkovChain.b(int).
max{ N, empiricalDist.nSamples }.
Option.quotientDelta(int,int) but the
* hedge is rebalanced when the next hedge trade is triggered.
RandomVariable providing
* only one method to compute the mean.FixedBinDataSource fed by the samples of a
random variable X conditioned on
information
available at time t.this).empiricalDistribution not initialized.
RandomVariablesRandomVariable
and implements componentwise expectations and standard deviations as well as
covariations and correlations between components.RF(p,q,K1,K2) receives
Libor delta_jL_j(T_j) and pays
delta_j*max{K1-L_j(T_j),K2} at time
T_{j+1}, j=p,p+1,...,q-1.L_j needed for j>=p and until
time min(T_q,T_{n-1}).
vertex[i] is reflected at the
barycenter of the convex hull of the remaining vertices (the
opposing face) by a factor k.
x relative to
the vector y coordinate by coordinate rather than
through norms (global).
vertex[max] (the worst vertex) with newVertex
and updates the barycenter and function values.
rho_ij
for i,j>=1.
rho_ij of log-Libor increments for
0<=i,j<n.
n by n matrix of instantaneous log-Libor
correlations (rho_ij)_{0<=i,j<n}.
rho_ij
for i,j>=1.
dL_i,dL_j.
this=this*A.
this=A*this.
this=this*A.
this=A*this.
this.
this.
a(i), b(i).
reward(t,i)
* for stopping at time t if the chain is in state i.S_pq(t)=k(t,[T_p,T_q]) at time
t=0.
S_pq(t)=k(t,[T_p,T_q]) at
discrete time t=T_m.
<log(L_i)>_{T_t}^{T_i}=
int_{T_t}^{T_i}sigma_i^2(s)ds.L_i
until caplet expiration T_i.
<log(S_pq)>_t^T) of the swap rate S_pq
to expiration conditioned on the state of the Libor process
at time t.
<log(S_pq)>_0^{T_p}) to
expiration of the swap rate S_pq.
StatisticsStochasticProcess.timeStep(int) undefined.
reward(t,j) if the chain is stopped
* at time t in state j.swp([T_p,T_q],kappa) settled in arrears pays off
delta_k*(L_k(T_k)-kappa) at time T_{k+1} for
k=p,p+1,...,q-1.L_j, j>=p up to
time min{T_q,T_{n-1}}.
swpn(T,[T_p,T_q],k) pays off
h=B_pq(T)*(S_pq(T)-k)^+ at exercise time T=T_tau,
where k is the strike rate and S_pq(T) is
the value of the [T_p,T_q]-swap rate at time T.L_j, j>=tau
at time T_tau (forward transporting from time
T_tau).
CapletFile and SwaptionFile in the precise format
in which a Calibrator object expects these files for reading in the
data.CS_FactorLoading object of dimension
n.
CS_FactorLoading object of dimension
n.
CS_FactorLoading object of dimension
n.
this).
this).
this=alpha*this.
this=alpha*this and returns
the resulting vector code>this.
this=alpha*this.
this=alpha*this.
this[i]=alpha[i]*this[i].
Z=lambda*X, where X is
the current random variable this.
Optimizer.f(double[]).
a_i<=x_i<=;b_i for
a global minimum of the function Optimizer.f(double[]).
x minimizing the function
f(x).
this=z;.
Optimizer.f(double[]).
newWeight.
newWeight.
CS_FactorLoading.
setUp since the test fixture is static final.
setUp since the test fixture is static final.
setUp (test fixture is
static final since none of the tests
alters the basic data).
this.
h_t> Q(t)+alpha(t)
- shiftTrigger(double) -
Method in class Options.AmericanBlackScholesPut
- Triggers as soon as
h_t> Q(t)+alpha(t)
- sigma(int, double) -
Method in class Libor.LiborProcess.CS_FactorLoading
- Volatility
sigma_i(t) of log(L_i(t)),
defined on [0,T_i].
- sigma(int, double) -
Method in class Libor.LiborProcess.EP_FactorLoading
- Volatility sigma_i(t) of log(L_i(t)), defined on [0,T_i].
- sigma(int, double) -
Method in class Libor.LiborProcess.FactorLoading
- Volatility
sigma_i(t) of forward Libor L_i(t)
See document LiborProcess.ps
- sigma(int, double) -
Method in class Libor.LiborProcess.JR_FactorLoading
- Volatility
sigma_i(t) of log(L_i(t)),
defined on [0,T_i].
- sigma(int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The deterministic volatility
sigma_i(t) of Libor
L_i(t).
- sigma(int) -
Method in class Market.ConstantVolBasket
- Volatility of the asset S_i at time t.
- sigma(int) -
Method in class Market.DeterministicVolAsset
- Volatility at discrete time t (continuous time t*dt).
- sigma(int, int) -
Method in class Market.DeterministicVolBasket
- Volatility of the asset S_i at time t.
- simulationInit(int) -
Method in class Market.Asset
- Sets up a path simulation (t=0) or a simulation of
branches of an existing path (t>0, conditional expectations).
- simulationInit(int) -
Method in class Market.Basket
- Sets up a path simulation (t=0) or a simulation of
branches of an existing path (t>0, conditional expectations).
- simulationInit(int) -
Method in class Market.ConstantVolatilityAsset
- Sets pathCounter (if t=0) or branchCounter (if t>0) to zero.
- simulationInit(int) -
Method in class Market.ConstantVolatilityAssetQMC
- Allocates a quasi normal generator of the correct dimension to drive
a branch simulation.
- simulationInit(int) -
Method in class Market.DeterministicVolAsset
- Sets pathCounter (if t=0) or branchCounter (if t>0) to zero.
- simulationInit(int) -
Method in class Processes.StochasticProcess
- Sets up a path simulation (t=0) or a simulation of
* branches of an existing path (t>0, conditional expectations).
- simulationInit(int) -
Method in class Processes.VectorProcess
- Sets up a path simulation (t=0) or a simulation of
* branches of an existing path (t>0, conditional expectations).
- sleep(long) -
Method in class Examples.Probability.DirichletDemo
-
- sqrt(double[]) -
Static method in class Statistics.Vector
- Componentwise square root.
- standardDeviationOfReturn(double, double) -
Method in class Market.DeterministicVolAsset
- Standard deviation of the return process over the time
interval [a,b].
- stepToHorizonSimulationInit(int, int) -
Method in class Market.Basket
- Sets up a path simulation reaching the horizon T in one step from
the current time t.
- stepToHorizonSimulationInit(int, int) -
Method in class Market.ConstantVolBasket
- Must be called before each simulation of path branches which step
from current time t to the horizon T in one time step.
- stepToHorizonSimulationInit(int, int) -
Method in class Market.DeterministicVolBasket
- Must be called before each simulation of path branches which step
from current time t to the horizon T in one time step.
- stop(int) -
Method in class Processes.FirstExitTime_1D
- Stop as soon as X(t) exits D or t=horizon.
- stop(int) -
Method in class Processes.FirstExitTime_nD
- stop as soon as X(t) exits D or t=horizon.
- stop(int) -
Method in class Processes.HittingTime_1D
- stop as soon as X(t) hits D or t=horizon.
- stop(int) -
Method in class Processes.HittingTime_nD
- Stop as soon as X(t) hits D or t=horizon.
- stop(int) -
Method in interface Processes.StoppingTime
- Returns true if it is time stop at current time t,
* false otherwise.
- subtract(double[], double[]) -
Static method in class Statistics.Vector
- Subtracts Y from X and returns the updated double[ ] X.
- swRate(int, int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The forward swap rate
S_pq(t)=k(t,[T_p,T_q]) at discrete
time t (continuous time T_t).
- swapAnalyticForwardPrice(int, int) -
Method in class Libor.LiborDerivatives.BermudanSwaption
- Analytic forward price of the payer swap
swpn_t(kappa,[T_j,T_n]).
- swapRate(int, int) -
Method in class Libor.LiborProcess.Calibrator
- The forward swap rate
S_pq(t)=k(t,[T_p,T_q]) at time
t=0.
- swapRate(int, int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The forward swap rate
S_pq(t)=k(t,[T_p,T_q]) at discrete
time t (continuous time T_t).
- swapRate(int, int) -
Method in class Libor.LiborProcess.LiborProcess
- The forward swap rate
S_pq(t)=k(t,[T_p,T_q]) at time
t=0.
- swaptionAnalyticForwardPrice(int, int) -
Method in class Libor.LiborDerivatives.BermudanSwaption
- Analytic approximation to the price of the payer swaption
swpn_t(kappa,[T_j,T_n]).
- swaptionPrice(int, int, double) -
Method in class Libor.LiborProcess.Calibrator
- Analytic approximation to the swaption price.
- swaptions -
Static variable in class Libor.LiborProcess.Calibrator
- list of swaptions
- symmetricPlusEquals(double, double, ExtendedColtMatrix, boolean, ExtendedColtVector) -
Method in class LinAlg.ExtendedColtVector
- Implements the operation
this=beta*this+alpha*A*z.
- symmetricPlusEquals(double, ExtendedColtMatrix, boolean, ExtendedColtVector) -
Method in class LinAlg.ExtendedColtVector
- Implements the operation
this+=alpha*A*z.
- symmetricTimesEquals(ColtMatrix, boolean) -
Method in class LinAlg.ColtVector
- Implements the operation
this=A*this.
- symmetricTimesEquals(ExtendedColtMatrix, boolean, ColtVector) -
Method in class LinAlg.ExtendedColtVector
- Implements the operation
this=A*this.
StrategyAverageDown trading a
ConstantVolatilityAsset.TradingStrategiescurrentWeight
[T_p,T_q] with trigger level
K and strike rate kappa is triggered at the first
time T_j such that L_j(T_j)>K and then
initiates a swap swap([T_j,T_q],kappa).L_j needed for j>=p and until time
min(T_q,T_{n-1}).
- Triggers - package Triggers
- Package description:
Triggers - tearDown() -
Method in class Libor.LiborDerivatives.CapletTest
- Do nothing on
tearDown since none of the tests
alters the basic data.
- tearDown() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Do nothing on
tearDown since none of the tests
alters the basic data.
- tearDown() -
Method in class Libor.LiborProcess.CalibratorTest
- Do nothing on
tearDown since none of the tests
alters the basic data.
- tearDown() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Do nothing on
tearDown since none of the tests
alters the basic data.
- tearDown() -
Method in class Libor.LiborProcess.LiborProcessTest
- Do nothing on
tearDown since none of the tests
alters the basic data.
- tenorStructure() -
Method in class Libor.LiborProcess.LiborProcess
- The array
Tc[j]=T_j (continuous times).
- test(RandomVariable, int) -
Static method in class RandomVariables.MeanAndVarianceTest
-
- testBasic() -
Method in class LinAlg.ColtMatrixTest
- Test zero entry allocation, cloning, transpose, linear system
solution, inverse.
- testCapletForwardPrice() -
Method in class Libor.LiborDerivatives.CapletTest
- Test the computation of caplet implied vols.
- testCapletImpliedSigma() -
Method in class Libor.LiborProcess.CalibratorTest
- Test the caplet implied volatilities computed from analytic
caplet prices.
- testCholeskyRoot() -
Method in class LinAlg.ColtMatrixTest
- Test the Cholesky factorization
- testCholeskyRootMatrixSequence() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Sets up the array
L[t] of Cholesky roots of the
log-covariation-matrices.
- testCholeskyRootMatrixSequence() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Sets up the array
L[t] of Cholesky roots of the
log-covariation-matrices.
- testCorrelationMatrix() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Test if the correlation matrix is symmetric and positive definite
- testCorrelationMatrix() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Test if the correlation matrix is symmetric and positive definite
- testCovariationIntegrals() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Test the analytic covariation integrals against QMC numerical values.
- testCovariationIntegrals() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Test the analytic covariation integrals against QMC numerical values.
- testCovariationMatrixSequence() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Sets up the array
CV[t] of log-covariation-matrices.
- testCovariationMatrixSequence() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Sets up the array
CV[t] of log-covariation-matrices.
- testFactors() -
Static method in class Examples.Hedging.CallHedgeVariance
- Some Unit Tests
- testFmeans() -
Static method in class Examples.Hedging.CallHedgeVariance
- Tests the analytic formula for the mean
CallHedgeVariance.meanF1(int),
- testLogCovariationMatrices() -
Method in class Libor.LiborProcess.CS_FactorLoadingTest
- Tests if the
log-covariation-matrix is symmetric and positive definite.
- testLogCovariationMatrices() -
Method in class Libor.LiborProcess.JR_FactorLoadingTest
- Tests if the
log-covariation-matrix is symmetric and positive definite.
- testMonteCarloCapletImpliedSigma() -
Method in class Libor.LiborProcess.CalibratorTest
- Test the caplet implied volatilities computed from Monte Carlo
caplet prices.
- testSwapRates() -
Method in class Libor.LiborProcess.LiborProcessTest
- Tests the swap rates and annuities by comparing the streamlined
implementations against the straightforward ones over a sample
of ten paths.
- testX0LiborMeans() -
Method in class Libor.LiborProcess.LiborProcessTest
- Tests the mean of the Libor vector
X0LiborVector(p)
(X^0_p(T_p),X^0_{p+1}(T_p),....,X^0_{n-1}(T_p))
(both simulated directly and path simulated) against the known analytic
mean vector.
- testY0Covariances() -
Method in class Libor.LiborProcess.LiborProcessTest
- Tests the covariation matrix of the vector of
Y^0_j(T_p), j=p,...,q-1
(both simulated directly and path simulated) against the known
analytic covariation matrix.
- testZeroCouponBonds() -
Method in class Libor.LiborProcess.LiborProcessTest
- Tests if the general zero coupon bond
B(t,T) agrees
with the special bonds B(i,j)=B(T_i,T_j) in case
t=T_i, T=T_j.
- thebit(int) -
Static method in class Examples.QuasiMonteCarlo.GrayCodeCounter
-
- timeStep(int, int, boolean, boolean, boolean) -
Method in class Libor.LiborProcess.LiborProcess
- Evolves the X-Libors
L_j(t), j>=p from time
T_t (discrete time t) to time
T_{t+1} (discrete time t+1) in a single
time step.
- timeStep(int, boolean, boolean, boolean) -
Method in class Libor.LiborProcess.LiborProcess
- Evolves the full set of Libors from discrete time
t to time t+1 in a single time step.
- timeStep(int, int) -
Method in class Market.Asset
- Time step of riskfree bond and discounted asset price from discrete
time t to time t+1.
- timeStep(int, int, int) -
Method in class Market.Asset
- Single time step of riskfree bond and discounted asset price
from discrete time t to time s, skipping intermediate times if possible,
driven by new Brownian increments.
- timeStep(int, int) -
Method in class Market.Basket
- Time step of riskfree bond and discounted asset prices from discrete
time t to time t+1.
- timeStep(int, int) -
Method in class Market.ConstantVolBasket
- Time step t -> t+1 of the discounted asset prices.
- timeStep(int, int) -
Method in class Market.ConstantVolatilityAsset
- Time step t -> t+1 of discounted asset price path driven by a new
* (as opposed to sign changed) Z-increment.
- timeStep(int, int, int) -
Method in class Market.ConstantVolatilityAsset
- Single time step t -> s of discounted price path driven by new
* Z-increment.
- timeStep(int, int) -
Method in class Market.DeterministicVolAsset
- Time step t -> t+1 of discounted asset price path driven by a new
(as opposed to sign changed) Z-increment.
- timeStep(int, int, int) -
Method in class Market.DeterministicVolAsset
- Single time step t -> s of discounted price path driven by new
Z-increment.
- timeStep(int, int) -
Method in class Market.DeterministicVolBasket
- Time step t -> t+1 of the discounted asset prices.
- timeStep(int, int) -
Method in class Market.DiagonalBasket
-
- timeStep(int, int, int) -
Method in class Market.DiagonalBasket
-
- timeStep(int, int) -
Method in class Market.JumpAsset
- Time step t -> t+1 of discounted asset price path driven by a new
(as opposed to sign changed) Z-increment.
- timeStep(int, int, int) -
Method in class Market.JumpAsset
- Single time step t -> s of discounted price path driven by new
Z-increment.
- timeStep(int) -
Method in class Processes.BiasedRandomWalk
- The time step
StochasticProcess.timeStep(int).
- timeStep(int) -
Method in class Processes.BrownianMotion
- The time step
StochasticProcess.timeStep(int).
- timeStep(int) -
Method in class Processes.CompoundPoissonProcess
- The time step
StochasticProcess.timeStep(int).
- timeStep(int) -
Method in class Processes.MarkovChain
- Evolves the path of the chain from time t to time t+1.
- timeStep(int) -
Method in class Processes.SFSMarkovChain
- Evolves the path of the chain from time t to time t+1.
- timeStep(int) -
Method in class Processes.StochasticProcess
- Evolves a path from discrete time [0,t] to time t+1, that is,
* computes path[t+1] from path[u], u<=t.
- timeStep(int, int) -
Method in class Processes.StochasticProcess
- Computes path[s] from path[u], u<=t.
- timeStep(int) -
Method in class Processes.SymmetricRandomWalk
- The time step
StochasticProcess.timeStep(int).
- timeStep(int) -
Method in class Processes.VectorBrownianMotion
- The time step
VectorProcess.timeStep(int).
- timeStep(int) -
Method in class Processes.VectorProcess
- Evolves the path from discrete time [0,t] to time t+1,
* that is, computes path[t+1] from path[u], u<=t.
- timeStep(int, int) -
Method in class Processes.VectorProcess
- Computes path[s] from path[u], u<=t.
- timeStepToHorizon(int) -
Method in class Market.Basket
- Single time step of riskfree bond and discounted asset prices
from discrete time t to the horizon T, skipping intermediate times if
possible.
- timeStepToHorizon(int) -
Method in class Market.ConstantVolBasket
- Single time step t -> s of discounted asset price paths.
- timeStepToHorizon(int) -
Method in class Market.DeterministicVolBasket
- Single time step t -> s of discounted asset price paths.
- timesEquals(ColtMatrix) -
Method in class LinAlg.ColtVector
- Implements the operation
this=A*this and returns
the resulting vector code>this .
- timesEquals(ExtendedColtMatrix) -
Method in class LinAlg.ExtendedColtVector
- Implements the operation
this=A*this.
- toString(double[]) -
Static method in class ArrayClasses.ArrayUtils
- String representation of a one dimensional java arrray of doubles.
- toString(double[][]) -
Static method in class ArrayClasses.ArrayUtils
- String representation of a (ragged) two dimensional java arrray of
doubles.
- toString() -
Method in class ArrayClasses.LTRMatrixArray
- String representation for printing and inspection.
- toString() -
Method in class ArrayClasses.LowerTriangularArray
- String representation for printing and inspection.
- toString() -
Method in class ArrayClasses.UTRArray
- String representation for printing and inspection.
- toString() -
Method in class ArrayClasses.UTRMatrixArray
- String representation for printing and inspection.
- toString() -
Method in class ArrayClasses.UpperTriangularArray
- String representation for printing and inspection.
- toString() -
Method in class Libor.LiborProcess.CS_FactorLoading
- A message what type of factor loading it is, all the parameter values.
- toString() -
Method in class Libor.LiborProcess.EP_FactorLoading
- A message what type of factor loading it is, all the parameter values.
- toString() -
Method in class Libor.LiborProcess.FactorLoading
- String containing a message indicating what type of facto loading
it is, all the parameter values.
- toString() -
Method in class Libor.LiborProcess.JR_FactorLoading
- A message what type of factor loading it is, all the parameter values.
- toString() -
Method in class Libor.LiborProcess.LMM_Parameters
- A message containing all the parameters, initial Libors.
- toString() -
Method in class Libor.LiborProcess.LiborProcess
- A message what type of factor loading it is, all the parameter values.
- tradeStatistics() -
Method in class TradingStrategies.TradingStrategy
- The
TradingStrategy.newTradeStatistics() as a random vector X:
- tradeStatistics() -
Method in class TradingStrategies.VectorStrategy
- The
VectorStrategy.newTradeStatistics() as a random vector X:
- transpose() -
Method in class LinAlg.ColtMatrix
- Returns the transpose of
this.
- transpose() -
Method in class LinAlg.ExtendedColtMatrix
- Returns the transpose of
this.
- transposeSelf() -
Method in class LinAlg.ColtMatrix
- Transposes
this.
- transposeSelf() -
Method in class LinAlg.ExtendedColtMatrix
- Transposes
this.
- twopower(int) -
Static method in class Examples.QuasiMonteCarlo.GrayCodeCounter
-
(a_ij)_{p<=i<=j< n}
stored as straightforward ragged java array.A of n-1 lower triangular matrices
(arrays).new.
(a_ij)_{0<=i<=j
stored as straightforward ragged java array.- UpperTriangularArray(int) -
Constructor for class ArrayClasses.UpperTriangularArray
- Memory allocation, all entries zero.
- Urns - class Examples.Probability.Urns.
- Two urns are filled with 100 white respectively 200 black balls.
- Urns() -
Constructor for class Examples.Probability.Urns
-
- underlyingIsCVA() -
Method in class Options.Option
- Returns
true if the underlying is a
* ConstantVolatilityAsset, false otherwise.
- underlyingIsDividendFreeCVA() -
Method in class Options.Option
- Returns
true if the underlying is a dividend free
* ConstantVolatilityAsset, false otherwise.
- uniform_1 -
Static variable in class Statistics.Random
- First MersenneTwister uniform random number generator.
- uniform_2 -
Static variable in class Statistics.Random
- Second MersenneTwister uniform random number generator.
- upperBound(int, Trigger) -
Method in class Options.AmericanBasketOption
- This computes the upper bound
U_0+E(Sum_{t for the option price
V_0.
- upperBound(int, Trigger) -
Method in class Options.AmericanOption
- This computes the upper bound
U_0+Sum_{t where
K_t=(E_t[U_{t+1}-U_t])^+) for the option price
V_0.
Hedge using a delta hedge (defined in the package
TradingStrategies)
as the trading strategy hedging the option payoff.VectorProcess.timeStep(int) undefined.
Basket (vector of baskets).currentWeight
this at the point x.
this) computed from the
current path of the underlying process
L_i
w^{p,q}_j(t) in the representation of the
swap rate S_pq(t) as a convex combination of Libors.
w^{p,q}_j(t) in the representation of the
swap rate S_pq(t) as a convex combination of Libors.
w^{p,q}_j(t) in the representation of the
swap rate S_pq(t) as a convex combination of Libors at
time t=0.
str into a field of width fw,
ie.
str into a field of width
this.w,
ie.
X_j(t)=delta_jL_j(t), value in current path.
X^0_j(t), value in current path.
(X^0_p(T_p),...,X^0_{n-1}(T_p), that is,
snapshot of the log-normal X0 Libors
X^0_j, j=p,p+1,...,n-1 at time T_p.
X^1_j(t), value in current path.
x[j]=X_j(0) of initial X-Libors.
x_pq(t) used in the computation of the
approximation of the volatility of the swap rate S_pq.
x_pq(t) used in the computation of the
approximation of the swap rate volatility.
x_pq(0) used in the computation
of the approximation of the swap rate volatility.
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