Price Equity, FX, Commodity, or Energy Instruments
An equity derivative is a contract whose value is at least partly derived from one or more underlying equity, foreign exchange (FX), commodity, or energy securities. This toolbox provides functionality to price, compute sensitivity and hedging analysis to many equity securities. You can price vanilla, Asian, lookback, barrier, and spread options with pricing models that include lattice models, Monte Carlo simulations, multiple closed-form solutions, and finite differences methods.
The object-based framework supports a workflow for creating instruments, models, and pricer objects to price financial instruments. Using these objects, you can price interest-rate, inflation, equity, commodity, FX, or credit derivative instruments. The object-based workflow is an alternative to pricing financial instruments using functions. Working with modular objects for instruments, models, and pricers, you can easily reuse these objects to compare instrument prices for different models and pricing engines. You can use the object-based workflow to price a single instrument or to price a collection of instruments in a portfolio. For more information on the workflow, see Get Started with Workflows Using Object-Based Framework for Pricing Financial Instruments.
Create an equity, FX, or commodity instrument object using fininstrument,
then associate a model using finmodel, and then
specify a pricing method using finpricer.
Functions
Live Editor Tasks
| Calibrate Pricing Model | Calibrate option pricing model in the Live Editor (Since R2022a) |
Objects
Topics
- Price Spread Instrument for a Commodity Using BlackScholes Model and Analytic Pricers
This example shows the workflow to price a commodity
Spreadinstrument when you use aBlackScholesmodel andKirkandBjerksundStenslandanalytic pricing methods. - Price European Vanilla Call Options Using Black-Scholes Model and Different Equity Pricers
This example shows how to compare European
Vanillainstrument call option prices using aBlackScholesmodel and different pricing methods. - Use Black-Scholes Model to Price Asian Options with Several Equity Pricers
This example shows how to compare arithmetic and geometric Asian option prices using the
BlackScholesmodel and various pricing methods. - Hedge Options Using Reinforcement Learning Toolbox
This example shows how to outperform the traditional BSM approach using an optimal option hedging policy.
- Use Deep Learning to Approximate Barrier Option Prices with Heston Model
This example shows how to use Deep Learning Toolbox™ to train a network and obtain predictions on barrier option prices with a Heston model.
- Calibrate Option Pricing Model Using Heston Model
This example shows how to use the Calibrate Pricing Model Live Editor task to calibrate a
Hestonpricing model to call option prices from the market. - Price Weather Derivatives
This example demonstrates a workflow for pricing weather derivatives based on historically observed temperature data.
- Using Extreme Value Theory and Copula Fitting to Generate Synthetic Data
This example shows the workflow for generating synthetic equity index return data using Extreme Value Theory (EVT) and a copula model.
- Get Started with Workflows Using Object-Based Framework for Pricing Financial Instruments
Use objects to model and price financial instruments.
- Choose Instruments, Models, and Pricers
Select instruments, associated models, and associated pricers.
- Supported Exercise Styles
The following table lists the interest-rate instrument objects with their associated models and pricers and supported
Exercisestyles. - Mapping Financial Instruments Toolbox Functions to Object-Based Framework for Instruments, Models, and Pricers
Mapping functions to a workflow using objects for instruments, models, and pricers.
Related Information
Featured Examples
Price Vanilla Instrument Using Heston Model and Multiple Different Pricers
The workflow to price a Vanilla instrument when you use a Heston model and various pricing methods.
Price European Vanilla Call Options Using Black-Scholes Model and Different Equity Pricers
Compare European Vanilla instrument call option prices using a BlackScholes model and different pricing methods. The pricing methods for this comparison are the Cox-Ross-Rubinstein, Leisen-Reimer, finite difference, and the Black-Scholes analytical formula.
Use Black-Scholes Model to Price Asian Options with Several Equity Pricers
Compare arithmetic and geometric Asian option prices using the BlackScholes model and various pricing methods. The pricing methods are: the Kemna-Vorst, Levy, Turnbull-Wakeman, and Cox-Ross-Rubinstein methods and Monte Carlo simulation. This example also demonstrates how variations in spot prices affect option and delta sensitivity values on European vanilla and Asian options.
Create and Price Portfolio of Instruments
Use finportfolio and pricePorfolio to create and price a portfolio of interest-rate and equity instruments. The portfolio contains a vanilla FixedBond, an OptionEmbeddedFixedBond, a Vanilla European call option, a Vanilla American call option, and an Asian call option.
Price Spread Instrument for a Commodity Using BlackScholes Model and Analytic Pricers
The workflow to price a commodity Spread instrument when you use a BlackScholes model and Kirk and BjerksundStensland analytic pricing methods.
Hedge Options Using Reinforcement Learning Toolbox
Outperform the traditional BSM approach using an optimal option hedging policy.
Use Deep Learning to Approximate Barrier Option Prices with Heston Model
Use Deep Learning Toolbox™ to train a network and obtain predictions on barrier option prices with a Heston model.
Calibrate Option Pricing Model Using Heston Model
Use the Calibrate Pricing Model Live Editor task to calibrate a Heston pricing model to call option prices from the market. After calibration, use the Financial Instruments Toolbox™ object-based workflow to price an American option for a Barrier instrument using the calibrated parameter values for the Heston model with an AssetMonteCarlo pricing method.
Price Weather Derivatives
Demonstrates a workflow for pricing weather derivatives based on historically observed temperature data.
Volatility Modeling for Soft Commodities
Demonstrates a diverse set of statistical methods, machine learning techniques, and time-series models that you can apply broadly in the field of volatility modeling. Specifically, this example focuses on the analysis, modeling, and forecasting of volatility within the context of soft commodities.
Using Extreme Value Theory and Copula Fitting to Generate Synthetic Data
The workflow for generating synthetic equity index return data using Extreme Value Theory (EVT) and a copula model. The objective of this example is to overcome challenges that are often encountered in financial return series, such as heteroskedasticity and fat tails. EVT focuses on the tails (the far left or right ends) of distribution curves, where extreme values lie. These values occur with very low probability but have a significant impact when they do occur. By applying EVT, you can effectively model the tail behavior of the equity index returns, which is crucial in capturing extreme events. Additionally, you can use copula fitting to address the dependence structure among the individual components of the returns that accounts for their correlated behavior. Copula fitting involves selecting a copula model that best represents the observed dependence structure in the data and estimating its parameters.
