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spreadsensbyls

Calculate price and sensitivities for European or American spread options using Monte Carlo simulations

Syntax

PriceSens = spreadsensbyls(RateSpec,StockSpec1,StockSpec2,Settle,Maturity,OptSpec,Strike,Corr)
PriceSens = spreadsensbyls(___,Name,Value)
[PriceSens,Paths,Times,Z] = spreadsensbyls(RateSpec,StockSpec1,StockSpec2,Settle,Maturity,OptSpec,Strike,Corr)
[PriceSens,Paths,Times,Z] = spreadsensbyls(___,Name,Value)

Description

example

PriceSens = spreadsensbyls(RateSpec,StockSpec1,StockSpec2,Settle,Maturity,OptSpec,Strike,Corr) returns the price of a European or American call or put spread option using Monte Carlo simulations.

For American options, the Longstaff-Schwartz least squares method is used to calculate the early exercise premium.

PriceSens = spreadsensbyls(___,Name,Value) adds optional name-value pair arguments.

[PriceSens,Paths,Times,Z] = spreadsensbyls(RateSpec,StockSpec1,StockSpec2,Settle,Maturity,OptSpec,Strike,Corr) returns the PriceSens, Paths, Times, and Z of a European or American call or put spread option using Monte Carlo simulations.

[PriceSens,Paths,Times,Z] = spreadsensbyls(___,Name,Value) returns the PriceSens, Paths, Times, and Z and adds optional name-value pair arguments.

Examples

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Define the spread option dates.

Settle = '01-Jun-2012';
Maturity = '01-Sep-2012';

Define asset 1. Price and volatility of RBOB gasoline

  Price1gallon = 2.85;          % $/gallon
  Price1 = Price1gallon * 42;   % $/barrel
  Vol1 = 0.29;

Define asset 2. Price and volatility of WTI crude oil

  Price2 = 93.20;         % $/barrel
  Vol2 = 0.36;

Define the correlation between the underlying asset prices of asset 1 and asset 2.

Corr = 0.42;

Define the spread option.

OptSpec = 'call';
Strike = 20;

Define the RateSpec.

rates = 0.05;
Compounding = -1;
Basis = 1;
RateSpec = intenvset('ValuationDate', Settle, 'StartDates', Settle, ...
'EndDates', Maturity, 'Rates', rates, ...
'Compounding', Compounding, 'Basis', Basis)
RateSpec = struct with fields:
           FinObj: 'RateSpec'
      Compounding: -1
             Disc: 0.9876
            Rates: 0.0500
         EndTimes: 0.2500
       StartTimes: 0
         EndDates: 735113
       StartDates: 735021
    ValuationDate: 735021
            Basis: 1
     EndMonthRule: 1

Define the StockSpec for the two assets.

StockSpec1 = stockspec(Vol1, Price1)
StockSpec1 = struct with fields:
             FinObj: 'StockSpec'
              Sigma: 0.2900
         AssetPrice: 119.7000
       DividendType: []
    DividendAmounts: 0
    ExDividendDates: []

StockSpec2 = stockspec(Vol2, Price2)
StockSpec2 = struct with fields:
             FinObj: 'StockSpec'
              Sigma: 0.3600
         AssetPrice: 93.2000
       DividendType: []
    DividendAmounts: 0
    ExDividendDates: []

Compute the spread option price and sensitivities using Monte Carlo simulation based on the Longstaff-Schwartz model.

OutSpec = {'Price', 'Delta', 'Gamma'};
[Price, Delta, Gamma] = spreadsensbyls(RateSpec, StockSpec1, StockSpec2, ...
Settle, Maturity, OptSpec, Strike, Corr, 'OutSpec', OutSpec)
Price = 11.0799
Delta = 

    0.6626   -0.5972

Gamma = 

    0.0209    0.0240

Input Arguments

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Interest-rate term structure (annualized and continuously compounded), specified by the RateSpec obtained from intenvset. For information on the interest-rate specification, see intenvset.

Data Types: struct

Stock specification for underlying asset 1. For information on the stock specification, see stockspec.

stockspec can handle other types of underlying assets. For example, for physical commodities the price is represented by StockSpec.Asset, the volatility is represented by StockSpec.Sigma, and the convenience yield is represented by StockSpec.DividendAmounts.

Data Types: struct

Stock specification for underlying asset 2. For information on the stock specification, see stockspec.

stockspec can handle other types of underlying assets. For example, for physical commodities the price is represented by StockSpec.Asset, the volatility is represented by StockSpec.Sigma, and the convenience yield is represented by StockSpec.DividendAmounts.

Data Types: struct

Settlement date for the spread option, specified as a date character vector or as a nonnegative scalar integer.

Data Types: char | double

Maturity date for spread option, specified as a date character vector or as a nonnegative scalar integer.

Data Types: char | double

Definition of option as 'call' or 'put', specified as a character vector.

Data Types: char

Option strike price value, specified as a scalar integer.

Data Types: single | double

Correlation between underlying asset prices, specified as a scalar integer.

Data Types: single | double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: PriceSens = spreadbyls(RateSpec,StockSpec1,StockSpec2,Settle,Maturity,OptSpec,Strike,Corr,'AmericanOpt',1)

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Option type, specified as a scalar integer flag with values:

  • 0 — European

  • 1 — American

Note

For American options, the Longstaff-Schwartz least squares method is used to calculate the early exercise premium. For more information on the least squares method, see https://people.math.ethz.ch/%7Ehjfurrer/teaching/LongstaffSchwartzAmericanOptionsLeastSquareMonteCarlo.pdf.

Data Types: single | double

Number of independent sample paths (simulation trials) specified as a nonnegative scalar integer.

Data Types: single | double

Number of simulation periods per trial, specified as a nonnegative scalar integer. NumPeriods is considered only when pricing European basket options. For American spread options, NumPeriods is equal to the number of exercise days during the life of the option.

Data Types: single | double

Time series array of dependent random variates, specified as a NumPeriods-by-2-by-NumTrials 3-D array. The Z value generates the Brownian motion vector (that is, Wiener processes) that drives the simulation.

Data Types: single | double

Indicator for antithetic sampling, specified with value of true or false.

Data Types: logical

Define outputs specifying NOUT- by-1 or 1-by-NOUT cell array of character vectors with possible values of 'Price', 'Delta', 'Gamma', 'Vega', 'Lambda', 'Rho', 'Theta', and 'All'.

OutSpec = {'All'} specifies that the output should be Delta, Gamma, Vega, Lambda, Rho, Theta, and Price, in that order. This is the same as specifying OutSpec to include each sensitivity:

Example: OutSpec = {'delta','gamma','vega','lambda','rho','theta','price'}

Data Types: char | cell

Output Arguments

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Expected price or sensitivities of the spread option, returned as a 1-by-1 array as defined by OutSpec.

Simulated paths of correlated state variables, returned as a NumPeriods + 1-by-2-by-NumTrials 3-D time series array. Each row of Paths is the transpose of the state vector X(t) at time t for a given trial.

Observation times associated with simulated paths, returned as a NumPeriods + 1-by-1 column vector of observation times associated with the simulated paths. Each element of Times is associated with the corresponding row of Paths.

Time series array of dependent random variates, returned as a NumPeriods-by-2-by-NumTrials 3-D array when Z is specified as an input argument. If the Z input argument is not specified, then the Z output argument contains the random variates generated internally.

References

[1] Carmona, R., Durrleman, V. “Pricing and Hedging Spread Options.” SIAM Review. Vol. 45, No. 4, pp. 627–685, Society for Industrial and Applied Mathematics, 2003.

Introduced in R2013b

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