Constructing Lattice Methods Using the Finance Package

Overview


There are three main numerical procedures that can be used to value derivatives when exact formulas are not available. These involve the use of recombining trees, Monte Carlo simulation, and finite difference methods. Monte Carlo simulation is used primarily for derivatives where the payoff is dependent on the history of the underlying variable, or where there are several underlying variables. Recombining trees and finite difference methods are particularly useful when the holder has early exercise decisions to make prior to maturity. In addition to valuing a derivative, all the procedures can be used to calculate the market sensitivities (or the Greeks) such as Delta, Gamma, and Vega.
The Financial Modeling package provides various tools for constructing binomial and trinomial tree approximations of stochastic processes. These include simple binomial trees such as JarrowRudd or CoxRossRubinstein, trinomial trees, implied binomial and trinomial trees as well as general treebuilding tools.
The following commands can be used to inspect/manipulate a tree data structure.
GetDescendants



return descendants for a node of a binomial or trinomial tree

GetProbabilities



return probabilities for a node of a binomial or trinomial tree

GetUnderlying



return the value of the underlying for a node of a binomial or trinomial tree

GetLocalVolatility



return the local volatility node of a BlackScholes binomial or trinomial tree

SetProbabilities



set probabilities for a node of a binomial or trinomial tree

SetUnderlying



set the value of the underlying for a node of a binomial or trinomial tree





Constant Volatility Binomial Trees


Binomial trees are frequently used to approximate the movements in the price of a stock or other asset under the BlackScholesMerton model. There are several approaches to building the underlying binomial tree, such as CoxRossRubinstein, JarrowRudd, and Tian. In all of the approaches above, the lattice is designed so as to minimize the discrepancy between the approximate (discrete) and target (continuous) distributions by matching, exactly or approximately, their first few moments. The rationale for this is that for any fixed number of time steps, a momentmatching lattice is believed to produce better option price estimates.
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If you denote by and the multiplicative constants for up and down movements in the tree, and by and the probabilities of the upward and the downward movements, then the stock price at the th time step and the th node is
for
and
You know that the three variables satisfy two equations, so there is some freedom to assign a value to one of the variables. This is the reason leading to the different versions of the binomial tree. In a riskneutral binomial tree the transition probabilities can then be determined from the noarbitrage condition
where is the known forward price of the stock. In a general constant volatility recombining binomial tree and have the form
and
for some reasonable value of .
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 (2.1) 
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 (2.2) 
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 (2.3) 
The corresponding transition probabilities are
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 (2.4) 
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 (2.5) 
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 (2.6) 
Consider the BlackScholes process with the following parameters:
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 (2.7) 
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 (2.8) 
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 (2.9) 
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 (2.10) 
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 (2.11) 

CoxRossRubinstein Binomial Tree


The binomial tree introduced by Cox, Ross and Rubinstein in 1979 (hereafter CRR) is one of the most important innovations to have appeared in the option pricing literature. Beyond its original use as a tool to approximate the prices of European and American options in the BlackScholes (1973) framework, it is also widely used as a pedagogical device to introduce various key concepts in option pricing. CRR presented the fundamental economic principles of option pricing by arbitrage considerations in the most simplest manner. By application of a central limit theorem, they proved that their model merges into the Black and Scholes model when the time steps between successive trading instances approach zero. Additionally, the model was used to evaluate American type options and options on assets with continuous dividend payments. The Cox, Ross, and Rubinstein model makes the multiplication of up and down jumps equal
,
and
This corresponds to the case when
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 (2.1.1) 
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Here is a logarithmic view of the same tree.
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 (2.1.2) 


JarrowRudd Binomial Tree


There exist many extensions of the CRR model. Jarrow and Rudd (1983), JR, adjusted the CRR model to account for the local drift term
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They constructed a binomial model where the first two moments of the discrete and continuous timereturn processes match. As a consequence, a probability measure equal to one half results. This corresponds to the case when .
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 (2.2.1) 
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 (2.2.2) 
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 (2.2.3) 
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Trinomial Trees


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 (3.1) 
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 (3.2) 
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 (3.3) 
The Financial Modeling package provides some tools for approximating diffusions using trinomial trees.
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Implied Binomial Trees


In this example you will construct an implied binomial tree and use it to price some other instruments.
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 (4.1) 
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 (4.2) 
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Here are two different views of the same tree; the first one uses the standard scale, the second one uses the logarithmic scale.
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Inspect the tree.
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 (4.3) 
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 (4.4) 
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 (4.5) 
Compare this tree with the standard CoxRossRubinstein binomial tree constructed for the volatility equal to sigma(0, 100).
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 (4.6) 
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 (4.7) 
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 (4.8) 
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 (4.9) 
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 (4.10) 
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 (4.11) 
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 (4.12) 
So, the results match in this case.
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 (4.13) 
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 (4.14) 
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 (4.15) 
You can price other kinds of options using this tree.
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 (4.16) 
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 (4.17) 
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 (4.18) 


References


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John C. Hull, Options, Futures, and Other Derivatives, Prentice Hall, 2002

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Global Derivatives, http://www.globalderivatives.com/

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MathFinance, http://www.mathfinance.de/

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Lishang Jiang, Mathematical Modeling and Methods of Option Pricing, Higher Education Press, 2003

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Michel Denault, Genevieve Gauthier, and JeanGuy Simonato, Improving Lattice Schemes Throught Bias Reduction, 2003

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Leisen, D. and Reimer, M. (1996). Binomial Models For Option Valuation  Examining and Improving Convergence. Applied Mathematical Finance 3 pp. 319346

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Trigeorgis, L., A LogTransformed Binomial Numerical Analysis Method for Valuing Complex MultiOption Investments, Journal of Financial and Quantitative Analysis, Vol. 26, pp. 309326, 1991


