public class BachelierModel extends Object
BachelierModel
is the Monte-Carlo
implementation of a Homogeneous Bachelier model.
The class InhomogenousBachelierModel
is the Monte-Carlo
implementation of a Inhomogeneous Bachelier model.BachelierModel
,
InhomogenousBachelierModel
Modifier and Type | Method and Description |
---|---|
static double |
bachelierHomogeneousOptionDelta(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierHomogeneousOptionDelta(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static double |
bachelierHomogeneousOptionImpliedVolatility(double forward,
double optionMaturity,
double optionStrike,
double payoffUnit,
double optionValue)
Calculates the Bachelier option implied volatility of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static double |
bachelierHomogeneousOptionValue(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierHomogeneousOptionValue(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static double |
bachelierHomogeneousOptionVega(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierHomogeneousOptionVega(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(rt) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma / N(T) \mathrm{d}W(t) \text{
|
static double |
bachelierInhomogeneousOptionDelta(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with constant volatility, i.e., a inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierInhomogeneousOptionDelta(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with constant volatility, i.e., a inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{
|
static double |
bachelierInhomogeneousOptionImpliedVolatility(double forward,
double optionMaturity,
double optionStrike,
double payoffUnit,
double optionValue)
Calculates the Bachelier option implied volatility of a call, i.e., the payoff
max(S(T)-K,0), where S follows a
normal process with constant volatility, i.e., a inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{
|
static double |
bachelierInhomogeneousOptionValue(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with constant volatility, i.e., a inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierInhomogeneousOptionValue(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with constant volatility, i.e., a inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{
|
static double |
bachelierInhomogeneousOptionVega(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with constant volatility, i.e., a Inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp( r t ) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(-r t) \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierInhomogeneousOptionVega(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with constant volatility, i.e., a Inhomogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp( r t ) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma exp(-r t) \mathrm{d}W(t) \text{
|
static double |
bachelierOptionDelta(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierOptionDelta(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option delta dV(0)/dS(0) of a call option, i.e., the payoff V(T)=max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static double |
bachelierOptionImpliedVolatility(double forward,
double optionMaturity,
double optionStrike,
double payoffUnit,
double optionValue)
Calculates the Bachelier option implied volatility of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static double |
bachelierOptionValue(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierOptionValue(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the option value of a call, i.e., the payoff max(S(T)-K,0), where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static double |
bachelierOptionVega(double forward,
double volatility,
double optionMaturity,
double optionStrike,
double payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
static RandomVariable |
bachelierOptionVega(RandomVariable forward,
RandomVariable volatility,
double optionMaturity,
double optionStrike,
RandomVariable payoffUnit)
Calculates the vega of a call, i.e., the payoff max(S(T)-K,0) P, where S follows a
normal process with numeraire scaled volatility, i.e., a homogeneous Bachelier model
\[
\mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma exp(-r (T-t)) \mathrm{d}W(t)
\]
Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows
\[
\mathrm{d} F(t) = \sigma \mathrm{d}W(t) \text{
|
public static double bachelierOptionValue(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierOptionValue(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierOptionImpliedVolatility(double forward, double optionMaturity, double optionStrike, double payoffUnit, double optionValue)
forward
- The forward of the underlying.optionMaturity
- The option maturity T.optionStrike
- The option strike. If the option strike is ≤ 0.0 the method returns the value of the forward contract paying S(T)-K in T.payoffUnit
- The payoff unit (e.g., the discount factor)optionValue
- The option value.public static double bachelierOptionDelta(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierOptionDelta(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierOptionVega(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierOptionVega(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierHomogeneousOptionValue(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierHomogeneousOptionValue(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierHomogeneousOptionImpliedVolatility(double forward, double optionMaturity, double optionStrike, double payoffUnit, double optionValue)
forward
- The forward of the underlying.optionMaturity
- The option maturity T.optionStrike
- The option strike. If the option strike is ≤ 0.0 the method returns the value of the forward contract paying S(T)-K in T.payoffUnit
- The payoff unit (e.g., the discount factor)optionValue
- The option value.public static double bachelierHomogeneousOptionDelta(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierHomogeneousOptionDelta(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierHomogeneousOptionVega(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static RandomVariable bachelierHomogeneousOptionVega(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F(0) = S(0)/N(0) = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \) of the forward process.optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor \( N(0)/N(T) = exp(-r T) \))public static double bachelierInhomogeneousOptionValue(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor)public static RandomVariable bachelierInhomogeneousOptionValue(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike.payoffUnit
- The payoff unit (e.g., the discount factor)public static double bachelierInhomogeneousOptionImpliedVolatility(double forward, double optionMaturity, double optionStrike, double payoffUnit, double optionValue)
max(S(T)-K,0)
, where S follows a normal process with constant volatility, i.e., a inhomogeneous Bachelier model \[ \mathrm{d} S(t) = r S(t) \mathrm{d} t + \sigma \mathrm{d}W(t) \] Considering the numeraire \( N(t) = exp(-r (T-t)) \), this implies that \( F(t) = S(t)/N(t) \) follows \[ \mathrm{d} F(t) = \sigma exp(r (T-t)) \mathrm{d}W(t) \text{.} \]forward
- The forward of the underlying.optionMaturity
- The option maturity T.optionStrike
- The option strike. If the option strike is ≤ 0.0 the method returns the value of the forward contract paying S(T)-K in T.payoffUnit
- The payoff unit (e.g., the discount factor)optionValue
- The option value.public static double bachelierInhomogeneousOptionDelta(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor)public static RandomVariable bachelierInhomogeneousOptionDelta(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike K.payoffUnit
- The payoff unit (e.g., the discount factor)public static double bachelierInhomogeneousOptionVega(double forward, double volatility, double optionMaturity, double optionStrike, double payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike.payoffUnit
- The payoff unit (e.g., the discount factor)public static RandomVariable bachelierInhomogeneousOptionVega(RandomVariable forward, RandomVariable volatility, double optionMaturity, double optionStrike, RandomVariable payoffUnit)
forward
- The forward of the underlying \( F = S(0) \exp(r T) \).volatility
- The Bachelier volatility \( \sigma \).optionMaturity
- The option maturity T.optionStrike
- The option strike.payoffUnit
- The payoff unit (e.g., the discount factor)Copyright © 2020 finmath.net. All rights reserved.