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Lattice model (finance)

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Title: Lattice model (finance)  
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Subject: Finite difference methods for option pricing, Trinomial tree, Exotic derivative, Embedded option, Valuation (finance)
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Lattice model (finance)

For other meanings, see lattice model

In finance, a lattice model is a discretization used to find the fair value of a stock option; variants also exist for interest rate derivatives. [1]

The model divides time between now and the option's expiration into N discrete periods. At the specific time n, the model has a finite number of outcomes at time n + 1 such that every possible change in the state of the world between n and n + 1 is captured in a branch. This process is iterated until every possible path between n = 0 and n = N is mapped. Probabilities are then estimated for every n to n + 1 path. The outcomes and probabilities flow backwards through the tree until a fair value of the option today is calculated.

Equity and commodity derivatives

Binomial Lattice with CRR formulae

The simplest lattice model for options on equity and commodities is the binomial options pricing model,[2] while a more sophisticated variant [3] is the Trinomial tree. As above, these models trace the evolution of the option's key underlying variable(s) in discrete-time, starting with today's spot price, and consistent with its volatility; log-normal Brownian motion with constant volatility is usually assumed.[4]

Lattice models are particularly useful in valuing American options, where the choice whether to exercise the option early, or to hold the option, may be modeled at each discrete time/price combination; this is true also for Bermudan options. See Binomial options pricing model#Method. For similar reasons, real options and employee stock options are often modeled using a lattice framework, though with modified assumptions. Some exotic options, such as barrier options, are also easily modeled here; note though that for other Path-Dependent Options, simulation would be preferred.

When it is important to incorporate the volatility smile, or surface, Implied trees can be constructed. Here, the tree is solved such that it successfully reproduces selected (all) market prices, across various strikes and expirations; see local volatility. Using the calibrated lattice one can then price options with strike / maturity combinations not quoted in the market, such that these prices are consistent with observed volatility patterns. Both Implied binomial trees (often Rubinstein IBTs [5]) and Implied trinomial trees (often Derman-Kani-Chriss [6]) exist. The former is easier built, but is consistent with one maturity only; the latter will be consistent with, but at the same time requires, known (or interpolated) prices at all time-steps.

As an alternative, Edgeworth binomial trees [7] allow for an analyst-specified skew and kurtosis in spot price returns; see Edgeworth series. This approach is useful when the underlying's behavior departs (markedly) from normality. A related use is to calibrate the tree to the volatility smile (or surface), by a "judicious choice" [8] of parameter values — priced here, options with differing strikes will return differing implied volatilities. For pricing American options, an Edgeworth-generated ending distribution may be combined with a Rubinstein IBT. Note that this approach is limited as to the set of skewness and kurtosis pairs for which valid distributions are possible. One recent proposal, Johnson binomial trees, is to use Johnson's system of distributions, as this is capable of accommodating all possible pairs; see Johnson SU distribution.

For multiple underlyers multinomial lattices [9][10] can be built, although the number of nodes increases exponentially with the number of underlyers. As an alternative, Basket options, for example, can be priced using an "approximating distribution" [11] via an Edgeworth (or Johnson) tree.

Interest rate derivatives

Interest rate lattices are commonly used in valuing Bond options, Swaptions, and other interest rate derivatives [12][13]

In these cases the lattice is built by discretizing either a short-rate model, such as Hull-White or Black Derman Toy, or a forward rate-based model, such as the LIBOR market model or HJM. As for equity, trinomial trees may also be employed for these models;[14] this is usually the case for Hull-White trees.

The short-rate lattices are, in turn, further categorized: these will be either equilibrium-based (Vasicek and CIR) or arbitrage-free (Ho–Lee and subsequent). This distinction means that for equilibrium-based models the yield curve is an output from the model, while for arbitrage-free models the yield curve is an input to the model.[15]

In the latter case, one "calibrates" the model parameters to fit both the current term structure of interest rates (i.e. the yield curve), and the corresponding volatility structure. Here, calibration means that the interest-rate-tree reproduces the prices of the zero-coupon bonds — and any other interest-rate sensitive securities — used in constructing the yield curve (note the analog to implied trees above). Once calibrated, the lattice can value a variety of more complex securities and derivatives. See Interest rate derivative#Exotic derivatives. The volatility structure — i.e. vertical node-spacing — here is usually based on interest rate caps, using volatility as implied by the Black-76-prices for each component caplet. For models assuming a normal distribution (such as Ho-Lee), calibration may be performed analytically, while for log-normal models the calibration is via a root-finding algorithm; see under Black–Derman–Toy model.

For the forward rate-based models, dependent on volatility assumptions, the lattice might not recombine. This means that an "up-move" followed by a "down-move" will not give the same result as a "down-move" followed by an "up-move". In this case, the Lattice is sometimes referred to as a bush, and the number of nodes grows exponentially as a function of number of time-steps.

References

  • Gerald Buetow and James Sochacki (2001). Term-Structure Models Using Binomial Trees. The Research Foundation of AIMR ( 
  • Clewlow, Les; Chris Strickland (1998). Implementing Derivative Models. New Jersey:  
  • Rama Cont, Ed. (2010). Tree methods in finance, Encyclopedia of Quantitative Finance. Wiley.  
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  • John van der Hoek and Robert J. Elliott (2006). Binomial Models in Finance. Springer.  
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