price
Compute price for equity instrument with NumericalIntegration
pricer
Syntax
Description
[
computes the instrument price and related pricing information based on the pricing object
Price,PriceResult] = price(inpPricer,inpInstrument)inpPricer and the instrument object
inpInstrument.
[
adds an optional argument to specify sensitivities.Price,PriceResult] = price(___,inpSensitivity)
Examples
This example shows the workflow to price a Vanilla instrument when you use a Merton model and a NumericalIntegration pricing method.
Create Vanilla Instrument Object
Use fininstrument to create a Vanilla instrument object.
VanillaOpt = fininstrument("Vanilla",'ExerciseDate',datetime(2020,3,15),'ExerciseStyle',"european",'Strike',105,'Name',"vanilla_option")
VanillaOpt =
Vanilla with properties:
OptionType: "call"
ExerciseStyle: "european"
ExerciseDate: 15-Mar-2020
Strike: 105
Name: "vanilla_option"
Create Merton Model Object
Use finmodel to create a Merton model object.
MertonModel = finmodel("Merton",'Volatility',0.45,'MeanJ',0.02,'JumpVol',0.07,'JumpFreq',0.09)
MertonModel =
Merton with properties:
Volatility: 0.4500
MeanJ: 0.0200
JumpVol: 0.0700
JumpFreq: 0.0900
Create ratecurve Object
Create a flat ratecurve object using ratecurve.
myRC = ratecurve('zero',datetime(2019,9,15),datetime(2020,3,15),0.02)myRC =
ratecurve with properties:
Type: "zero"
Compounding: -1
Basis: 0
Dates: 15-Mar-2020
Rates: 0.0200
Settle: 15-Sep-2019
InterpMethod: "linear"
ShortExtrapMethod: "next"
LongExtrapMethod: "previous"
Create NumericalIntegration Pricer Object
Use finpricer to create a NumericalIntegration pricer object and use the ratecurve object for the 'DiscountCurve'name-value pair argument.
outPricer = finpricer("numericalintegration",'Model',MertonModel,'DiscountCurve',myRC,'SpotPrice',100,'DividendValue',.01,'VolRiskPremium',0.9,'LittleTrap',false,'AbsTol',0.5,'RelTol',0.4,'Framework',"lewis2001")
outPricer =
NumericalIntegration with properties:
Model: [1×1 finmodel.Merton]
DiscountCurve: [1×1 ratecurve]
SpotPrice: 100
DividendType: "continuous"
DividendValue: 0.0100
AbsTol: 0.5000
RelTol: 0.4000
IntegrationRange: [1.0000e-09 Inf]
CharacteristicFcn: @characteristicFcnMerton76
Framework: "lewis2001"
VolRiskPremium: 0.9000
LittleTrap: 0
Price Vanilla Instrument
Use price to compute the price and sensitivities for the Vanilla instrument.
[Price, outPR] = price(outPricer,VanillaOpt,["all"])Price = 10.7325
outPR =
priceresult with properties:
Results: [1×6 table]
PricerData: []
outPR.Results
ans=1×6 table
Price Delta Gamma Theta Rho Vega
______ ______ ________ _______ ______ ______
10.732 0.5058 0.012492 -12.969 19.815 27.954
Input Arguments
Pricer object, specified as a scalar NumericalIntegration pricer object. Use finpricer to create the NumericalIntegration pricer object.
Data Types: object
Instrument object, specified as a scalar or vector of Vanilla instrument objects.
Use fininstrument to create
Vanilla instrument
objects.
Data Types: object
(Optional) List of sensitivities to compute, specified as a
NOUT-by-1 or a
1-by-NOUT cell array of character vectors or
string array with possible values of 'Price',
'Delta', 'Gamma', 'Vega',
'Rho', 'Theta', 'Vegalt', and
'All'.
inpSensitivity = {'All'} or inpSensitivity =
["All"] specifies that the output is 'Delta',
'Gamma', 'Vega', 'Rho',
'Theta', 'Vegalt', and
'Price'. This is the same as specifying
inpSensitivity to include each sensitivity.
Example: inpSensitivity =
{'delta','gamma','vega','rho','theta','vegalt','price'}
Data Types: string | cell
Output Arguments
Instrument price, returned as a numeric.
Price result, returned as an object. The PriceResult object. The
object has the following fields:
PriceResult.Results— Table of results that includes sensitivities (if you specifyinpSensitivity)PriceResult.PricerData— Structure for pricer data
More About
A delta sensitivity measures the rate at which the price of an option is expected to change relative to a $1 change in the price of the underlying asset.
Delta is not a static measure; it changes as the price of the underlying asset changes (a concept known as gamma sensitivity), and as time passes. Options that are near the money or have longer until expiration are more sensitive to changes in delta.
A gamma sensitivity measures the rate of change of an option's delta in response to a change in the price of the underlying asset.
In other words, while delta tells you how much the price of an option might move, gamma tells you how fast the option's delta itself will change as the price of the underlying asset moves. This is important because this helps you understand the convexity of an option's value in relation to the underlying asset's price.
A vega sensitivity measures the sensitivity of an option's price to changes in the volatility of the underlying asset.
Vega represents the amount by which the price of an option would be expected to change for a 1% change in the implied volatility of the underlying asset. Vega is expressed as the amount of money per underlying share that the option's value will gain or lose as volatility rises or falls.
A theta sensitivity measures the rate at which the price of an option decreases as time passes, all else being equal.
Theta is essentially a quantification of time decay, which is a key concept in options pricing. Theta provides an estimate of the dollar amount that an option's price would decrease each day, assuming no movement in the price of the underlying asset and no change in volatility.
A rho sensitivity measures the rate at which the price of an option is expected to change in response to a change in the risk-free interest rate.
Rho is expressed as the amount of money an option's price would gain or lose for a one percentage point (1%) change in the risk-free interest rate.
A vegalt sensitivity measures the sensitivity of an option's price to changes in the long-term volatility of the underlying asset.
Version History
Introduced in R2020a
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