Financial Instruments Toolbox™ supports the following interest-rate trees:

Black-Derman-Toy (BDT)

Black-Karasinski (BK)

Heath-Jarrow-Morton (HJM)

Hull-White (HW)

Cox-Ingersoll-Ross (CIR)

The Heath-Jarrow-Morton model is one of the most widely used models for
pricing interest-rate derivatives. The model considers a given initial term
structure of interest rates and a specification of the volatility of forward rates
to build a tree representing the evolution of the interest rates, based on a
statistical process. For further explanation, see the book *Modelling Fixed
Income Securities and Interest Rate Options * by Robert A.
Jarrow.

The Black-Derman-Toy model is another analytical model commonly used for pricing interest-rate derivatives. The model considers a given initial zero rate term structure of interest rates and a specification of the yield volatilities of long rates to build a tree representing the evolution of the interest rates. For further explanation, see the paper “A One Factor Model of Interest Rates and its Application to Treasury Bond Options” by Fischer Black, Emanuel Derman, and William Toy.

The Hull-White model incorporates the initial term structure of interest rates and the volatility term structure to build a trinomial recombining tree of short rates. The resulting tree is used to value interest rate-dependent securities. The implementation of the Hull-White model in Financial Instruments Toolbox software is limited to one factor.

The Black-Karasinski model is a single factor, log-normal version of the Hull-White model.

For further information on the Hull-White and Black-Karasinski
models, see the book *Options, Futures, and Other Derivatives* by
John C. Hull.

The tree of forward rates is the fundamental unit representing the evolution of interest rates in a given period of time. This section explains how to create a forward-rate tree using Financial Instruments Toolbox.

**Note**

To avoid needless repetition, this document uses the HJM and BDT models to illustrate the creation and use of interest-rate trees. The HW and BK models are similar to the BDT model. Where specific differences exist, they are documented in HW and BK Tree Structures.

The MATLAB^{®} functions that create rate trees are `hjmtree`

and `bdttree`

. The `hjmtree`

function creates the structure, `HJMTree`

,
containing time and forward-rate information for a bushy tree. The `bdttree`

function creates a similar structure, `BDTTree`

,
for a recombining tree.

This structure is a self-contained unit that includes the tree
of rates (found in the `FwdTree`

field of the structure)
and the volatility, rate, and time specifications used in building
this tree.

These functions take three structures as input arguments:

The volatility model

`VolSpec`

. (See Specifying the Volatility Model (VolSpec).)The interest-rate term structure

`RateSpec`

. (See Specifying the Interest-Rate Term Structure (RateSpec).)The tree time layout

`TimeSpec`

. (See Specifying the Time Structure (TimeSpec).)

An easy way to visualize any trees you create is with the `treeviewer`

function, which displays trees
in a graphical manner. See Graphical Representation of Trees for information about `treeviewer`

.

The calling syntax for `hjmtree`

is ```
HJMTree = hjmtree(VolSpec,
RateSpec, TimeSpec)
```

.

Similarly, the calling syntax for `bdttree`

is ```
BDTTree
= bdttree(VolSpec, RateSpec, TimeSpec)
```

.

Each of these functions requires `VolSpec`

, `RateSpec`

,
and `TimeSpec`

input arguments:

`VolSpec`

is a structure that specifies the forward-rate volatility process. You create`VolSpec`

using either of the functions`hjmvolspec`

or`bdtvolspec`

.The

`hjmvolspec`

function supports the specification of up to three factors. It handles these models for the volatility of the interest-rate term structure:Constant

Stationary

Exponential

Vasicek

Proportional

A one-factor model assumes that the interest term structure is affected by a single source of uncertainty. Incorporating multiple factors allows you to specify different types of shifts in the shape and location of the interest-rate structure. See

`hjmvolspec`

for details.The

`bdtvolspec`

function supports only a single volatility factor. The volatility remains constant between pairs of nodes on the tree. You supply the input volatility values in a vector of decimal values. See`bdtvolspec`

for details.`RateSpec`

is the interest-rate specification of the initial rate curve. You create this structure with the function`intenvset`

. (See Modeling the Interest-Rate Term Structure.)`TimeSpec`

is the tree time layout specification. You create this variable with the functions`hjmtimespec`

or`bdttimespec`

. It represents the mapping between level times and level dates for rate quoting. This structure indirectly determines the number of levels in the tree.

Because HJM supports multifactor (up to 3) volatility models
while BDT (also, BK and HW) supports only a single volatility factor,
the `hjmvolspec`

and `bdtvolspec`

functions require different
inputs and generate slightly different outputs. For examples, see Creating an HJM Volatility Model. For BDT examples, see Creating a BDT Volatility Model.

The function `hjmvolspec`

generates
the structure `VolSpec`

,
which specifies the volatility process $$\sigma \left(t,T\right)$$ used in the creation
of the forward-rate trees. In this context capital *T* represents
the starting time of the forward rate, and *t* represents
the observation time. The volatility process can be constructed from
a combination of factors specified sequentially in the call to function
that creates it. Each factor specification starts with a character
vector specifying the name of the factor, followed by the pertinent
parameters.

**HJM Volatility Specification Example. **Consider an example that uses a single factor, specifically,
a constant-sigma factor. The constant factor specification requires
only one parameter, the value of $$\sigma $$. In this case,
the value corresponds to 0.10.

`HJMVolSpec = hjmvolspec('Constant', 0.10)`

HJMVolSpec = FinObj: 'HJMVolSpec' FactorModels: {'Constant'} FactorArgs: {{1x1 cell}} SigmaShift: 0 NumFactors: 1 NumBranch: 2 PBranch: [0.5000 0.5000] Fact2Branch: [-1 1]

The `NumFactors`

field of the `VolSpec`

structure, ```
VolSpec.NumFactors
= 1
```

, reveals that the number of factors used to generate `VolSpec`

was
one. The `FactorModels`

field indicates that it is
a `Constant`

factor, and the `NumBranches`

field
indicates the number of branches. As a consequence, each node of the
resulting tree has two branches, one going up, and the other going
down.

Consider now a two-factor volatility process made from a proportional factor and an exponential factor.

% Exponential factor Sigma_0 = 0.1; Lambda = 1; % Proportional factor CurveProp = [0.11765; 0.08825; 0.06865]; CurveTerm = [ 1 ; 2 ; 3 ]; % Build VolSpec HJMVolSpec = hjmvolspec('Proportional', CurveProp, CurveTerm,... 1e6,'Exponential', Sigma_0, Lambda)

HJMVolSpec = FinObj: 'HJMVolSpec' FactorModels: {'Proportional' 'Exponential'} FactorArgs: {{1x3 cell} {1x2 cell}} SigmaShift: 0 NumFactors: 2 NumBranch: 3 PBranch: [0.2500 0.2500 0.5000] Fact2Branch: [2x3 double]

The output shows that the volatility specification was generated using two factors. The tree has three branches per node. Each branch has probabilities of 0.25, 0.25, and 0.5, going from top to bottom.

The function `bdtvolspec`

generates the structure `VolSpec`

,
which specifies the volatility process. The function requires three
input arguments:

The valuation date

`ValuationDate`

The yield volatility end dates

`VolDates`

The yield volatility values

`VolCurve`

An optional fourth argument `InterpMethod`

,
specifying the interpolation method, can be included.

The syntax used for calling `bdtvolspec`

is:

```
BDTVolSpec = bdtvolspec(ValuationDate, VolDates, VolCurve,...
InterpMethod)
```

where:

`ValuationDate`

is the first observation date in the tree.`VolDates`

is a vector of dates representing yield volatility end dates.`VolCurve`

is a vector of yield volatility values.`InterpMethod`

is the method of interpolation to use. The default is`linear`

.

**BDT Volatility Specification Example. **Consider the following example:

ValuationDate = datenum('01-01-2000'); EndDates = datenum(['01-01-2001'; '01-01-2002'; '01-01-2003'; '01-01-2004'; '01-01-2005']); Volatility = [.2; .19; .18; .17; .16];

Use `bdtvolspec`

to create
a volatility specification. Because no interpolation method is explicitly
specified, the function uses the `linear`

default.

BDTVolSpec = bdtvolspec(ValuationDate, EndDates, Volatility)

BDTVolSpec = FinObj: 'BDTVolSpec' ValuationDate: 730486 VolDates: [5x1 double] VolCurve: [5x1 double] VolInterpMethod: 'linear'

The structure `RateSpec`

is
an interest term structure that defines the initial forward-rate specification
from which the tree rates are derived. Modeling the Interest-Rate Term Structure explains
how to create these structures using the function `intenvset`

, given the interest rates,
the starting and ending dates for each rate, and the compounding value.

Consider the following example:

Compounding = 1; Rates = [0.02; 0.02; 0.02; 0.02]; StartDates = ['01-Jan-2000'; '01-Jan-2001'; '01-Jan-2002'; '01-Jan-2003']; EndDates = ['01-Jan-2001'; '01-Jan-2002'; '01-Jan-2003'; '01-Jan-2004']; ValuationDate = '01-Jan-2000'; RateSpec = intenvset('Compounding',1,'Rates', Rates,... 'StartDates', StartDates, 'EndDates', EndDates,... 'ValuationDate', ValuationDate)

RateSpec = FinObj: 'RateSpec' Compounding: 1 Disc: [4x1 double] Rates: [4x1 double] EndTimes: [4x1 double] StartTimes: [4x1 double] EndDates: [4x1 double] StartDates: [4x1 double] ValuationDate: 730486 Basis: 0 EndMonthRule: 1

Use the function `datedisp`

to
examine the dates defined in the variable `RateSpec`

.
For example:

datedisp(RateSpec.ValuationDate) 01-Jan-2000

`TimeSpec`

)The structure `TimeSpec`

specifies
the time structure for an interest-rate tree. This structure defines
the mapping between the observation times at each level of the tree
and the corresponding dates.

`TimeSpec`

is built using either the `hjmtimespec`

or `bdttimespec`

function.
These functions require three input arguments:

The valuation date

`ValuationDate`

The maturity date

`Maturity`

The compounding rate

`Compounding`

For example, the syntax used for calling `hjmtimespec`

is

`TimeSpec = hjmtimespec(ValuationDate, Maturity, Compounding)`

where:

`ValuationDate`

is the first observation date in the tree.`Maturity`

is a vector of dates representing the cash flow dates of the tree. Any instrument cash flows with these maturities fall on tree nodes.`Compounding`

is the frequency at which the rates are compounded when annualized.

Calling the time specification creation functions with the same
data used to create the interest-rate term structure, `RateSpec`

builds
the structure that specifies the time layout for the tree.

**HJM Time Specification Example. **Consider the following example:

Maturity = EndDates; HJMTimeSpec = hjmtimespec(ValuationDate, Maturity, Compounding)

HJMTimeSpec = FinObj: 'HJMTimeSpec' ValuationDate: 730486 Maturity: [4x1 double] Compounding: 1 Basis: 0 EndMonthRule: 1

Maturities specified when building `TimeSpec`

need
not coincide with the `EndDates`

of the rate intervals
in `RateSpec`

. Since `TimeSpec`

defines
the time-date mapping of the tree, the rates in `RateSpec`

are
interpolated to obtain the initial rates with maturities equal to
those in `TimeSpec`

.

**Creating a BDT Time Specification. **Consider the following example:

Maturity = EndDates; BDTTimeSpec = bdttimespec(ValuationDate, Maturity, Compounding)

BDTTimeSpec = FinObj: 'BDTTimeSpec' ValuationDate: 730486 Maturity: [4x1 double] Compounding: 1 Basis: 0 EndMonthRule: 1

Use the `VolSpec`

, `RateSpec`

,
and `TimeSpec`

you have previously created as inputs
to the functions used to create HJM and BDT trees.

% Reset the volatility factor to the Constant case HJMVolSpec = hjmvolspec('Constant', 0.10); HJMTree = hjmtree(HJMVolSpec, RateSpec, HJMTimeSpec)

HJMTree = FinObj: 'HJMFwdTree' VolSpec: [1x1 struct] TimeSpec: [1x1 struct] RateSpec: [1x1 struct] tObs: [0 1 2 3] TFwd: {[4x1 double] [3x1 double] [2x1 double] [3]} CFlowT: {[4x1 double] [3x1 double] [2x1 double] [4]} FwdTree:{[4x1 double][3x1x2 double][2x2x2 double][1x4x2 double]}

Now use the previously computed values for `VolSpec`

, `RateSpec`

,
and `TimeSpec`

as input to the function `bdttree`

to create a BDT tree.

BDTTree = bdttree(BDTVolSpec, RateSpec, BDTTimeSpec)

BDTTree = FinObj: 'BDTFwdTree' VolSpec: [1x1 struct] TimeSpec: [1x1 struct] RateSpec: [1x1 struct] tObs: [0 1.00 2.00 3.00] TFwd: {[4x1 double] [3x1 double] [2x1 double] [3.00]} CFlowT: {[4x1 double] [3x1 double] [2x1 double] [4.00]} FwdTree: {[1.02] [1.02 1.02] [1.01 1.02 1.03] [1.01 1.02 1.02 1.03]}

When working with the models, Financial Instruments Toolbox uses trees to represent forward rates, prices, and so on. At the highest level, these trees have structures wrapped around them. The structures encapsulate information required to interpret completely the information contained in a tree.

Consider this example, which uses the interest rate and portfolio
data in the MAT-file `deriv.mat`

included in the
toolbox.

Load the data into the MATLAB workspace.

```
load deriv.mat
```

Display the list of the variables loaded from the MAT-file.

whos

Name Size Bytes Class Attributes BDTInstSet 1x1 15956 struct BDTTree 1x1 5138 struct BKInstSet 1x1 15946 struct BKTree 1x1 5904 struct CRRInstSet 1x1 12434 struct CRRTree 1x1 5058 struct EQPInstSet 1x1 12434 struct EQPTree 1x1 5058 struct HJMInstSet 1x1 15948 struct HJMTree 1x1 5838 struct HWInstSet 1x1 15946 struct HWTree 1x1 5904 struct ITTInstSet 1x1 12438 struct ITTTree 1x1 8862 struct ZeroInstSet 1x1 10282 struct ZeroRateSpec 1x1 1580 struct

You can now examine in some detail the contents of the `HJMTree`

structure
contained in this file.

HJMTree

HJMTree = FinObj: 'HJMFwdTree' VolSpec: [1x1 struct] TimeSpec: [1x1 struct] RateSpec: [1x1 struct] tObs: [0 1 2 3] TFwd: {[4x1 double] [3x1 double] [2x1 double] [3]} CFlowT: {[4x1 double] [3x1 double] [2x1 double] [4]} FwdTree:{[4x1 double][3x1x2 double][2x2x2 double][1x4x2 double]}

`FwdTree`

contains the actual forward-rate
tree. MATLAB represents it as a cell array with each cell array
element containing a tree level.

The other fields contain other information relevant to interpreting
the values in `FwdTree`

. The most important are `VolSpec`

, `TimeSpec`

,
and `RateSpec`

, which contain the volatility, time
structure, and rate structure information respectively.

**First Node. **Observe the forward rates in `FwdTree`

. The
first node represents the valuation date, `tObs = 0`

.

HJMTree.FwdTree{1}

ans = 1.0356 1.0468 1.0523 1.0563

**Note**

Financial Instruments Toolbox uses *inverse discount* notation
for forward rates in the tree. An inverse discount represents a factor
by which the current value of an asset is multiplied to find its future
value. In general, these forward factors are reciprocals of the discount
factors.

Look closely at the `RateSpec`

structure used
in generating this tree to see where these values originate. Arrange
the values in a single array.

[HJMTree.RateSpec.StartTimes HJMTree.RateSpec.EndTimes... HJMTree.RateSpec.Rates]

ans = 0 1.0000 0.0356 1.0000 2.0000 0.0468 2.0000 3.0000 0.0523 3.0000 4.0000 0.0563

If you find the corresponding inverse discounts of the interest
rates in the third column, you have the values at the first node of
the tree. You can turn interest rates into inverse discounts using
the function `rate2disc`

.

Disc = rate2disc(HJMTree.TimeSpec.Compounding,... HJMTree.RateSpec.Rates, HJMTree.RateSpec.EndTimes,... HJMTree.RateSpec.StartTimes); FRates = 1./Disc

FRates = 1.0356 1.0468 1.0523 1.0563

**Second Node. **The second node represents the first-rate observation time, `tObs = 1`

. This node displays two
states: one representing the branch going up and the other representing
the branch going down.

Note that `HJMTree.VolSpec.NumBranch = 2`

.

HJMTree.VolSpec

ans = FinObj: 'HJMVolSpec' FactorModels: {'Constant'} FactorArgs: {{1x1 cell}} SigmaShift: 0 NumFactors: 1 NumBranch: 2 PBranch: [0.5000 0.5000] Fact2Branch: [-1 1]

Examine the rates of the node corresponding to the up branch.

HJMTree.FwdTree{2}(:,:,1)

ans = 1.0364 1.0420 1.0461

Now examine the corresponding down branch.

HJMTree.FwdTree{2}(:,:,2)

ans = 1.0574 1.0631 1.0672

**Third Node. **The third node represents the second observation time, ```
tObs
= 2
```

. This node contains a total of four states, two representing
the branches going up and the other two representing the branches
going down. Examine the rates of the node corresponding to the up
states.

HJMTree.FwdTree{3}(:,:,1)

ans = 1.0317 1.0526 1.0358 1.0568

Next examine the corresponding down states.

HJMTree.FwdTree{3}(:,:,2)

ans = 1.0526 1.0738 1.0568 1.0781

**Isolating a Specific Node. **Starting at the third level, indexing within the tree cell array
becomes complex, and isolating a specific node can be difficult. The
function `bushpath`

isolates
a specific node by specifying the path to the node as a vector of
branches taken to reach that node. As an example, consider the node
reached by starting from the root node, taking the branch up, then
the branch down, and then another branch down. Given that the tree
has only two branches per node, branches going up correspond to a
1, and branches going down correspond to a 2. The path up-down-down
becomes the vector `[1 2 2]`

.

FRates = bushpath(HJMTree.FwdTree, [1 2 2])

FRates = 1.0356 1.0364 1.0526 1.0674

`bushpath`

returns the spot rates for all the nodes tapped by
the path specified in the input argument, the first one corresponding to the
root node, and the last one corresponding to the target node.

Isolating the same node using direct indexing obtains

HJMTree.FwdTree{4}(:, 3, 2)

ans = 1.0674

As expected, this single value corresponds to the last element
of the rates returned by `bushpath`

.

You can use these techniques with any type of tree generated with Financial Instruments Toolbox, such as forward-rate trees or price trees.

You can now examine in some detail the contents of the `BDTTree`

structure.

BDTTree

BDTTree = FinObj: 'BDTFwdTree' VolSpec: [1x1 struct] TimeSpec: [1x1 struct] RateSpec: [1x1 struct] tObs: [0 1.00 2.00 3.00] TFwd: {[4x1 double] [3x1 double] [2x1 double] [3.00]} CFlowT: {[4x1 double] [3x1 double] [2x1 double] [4.00]} FwdTree: {[1.10] [1.10 1.14] [1.10 1.14 1.19] [1.09 1.12 1.16 1.22]}

`FwdTree`

contains the actual rate tree. MATLAB represents
it as a cell array with each cell array element containing a tree
level.

The other fields contain other information relevant to interpreting
the values in `FwdTree`

. The most important are `VolSpec`

, `TimeSpec`

,
and `RateSpec`

, which contain the volatility, time
structure, and rate structure information respectively.

Look at the `RateSpec`

structure used in generating
this tree to see where these values originate. Arrange the values
in a single array.

[BDTTree.RateSpec.StartTimes BDTTree.RateSpec.EndTimes... BDTTree.RateSpec.Rates]

ans = 0 1.0000 0.1000 0 2.0000 0.1100 0 3.0000 0.1200 0 4.0000 0.1250

Look at the rates in `FwdTree`

. The first node
represents the valuation date, `tObs = 0`

. The second node represents `tObs = 1`

. Examine the rates at the
second, third, and fourth nodes.

BDTTree.FwdTree{2}

ans = 1.0979 1.1432

The second node represents the first observation time, `tObs = 1`

. This node contains a total
of two states, one representing the branch going up (`1.0979`

)
and the other representing the branch going down (`1.1432`

).

**Note**

The convention in this document is to display *prices* going
up on the upper branch. So, when displaying *rates*,
rates are falling on the upper branch and increasing on the lower
branch.

BDTTree.FwdTree{3}

ans = 1.0976 1.1377 1.1942

The third node represents the second observation time, t`Obs = 2`

. This node contains a total
of three states, one representing the branch going up (`1.0976`

),
one representing the branch in the middle (`1.1377`

)
and the other representing the branch going down (`1.1942`

).

BDTTree.FwdTree{4}

ans = 1.0872 1.1183 1.1606 1.2179

The fourth node represents the third observation time, `tObs = 3`

. This node contains a total
of four states, one representing the branch going up (`1.0872`

),
two representing the branches in the middle (`1.1183`

and `1.1606`

),
and the other representing the branch going down (`1.2179`

).

**Isolating a Specific Node. **The function `treepath`

isolates
a specific node by specifying the path to the node as a vector of
branches taken to reach that node. As an example, consider the node
reached by starting from the root node, taking the branch up, then
the branch down, and finally another branch down. Given that the tree
has only two branches per node, branches going up correspond to a
1, and branches going down correspond to a 2. The path up-down-down
becomes the vector `[1 2 2]`

.

FRates = treepath(BDTTree.FwdTree, [1 2 2])

FRates = 1.1000 1.0979 1.1377 1.1606

`treepath`

returns the
short rates for all the nodes tapped by the path specified in the input
argument, the first one corresponding to the root node, and the last one
corresponding to the target node.

The HW and BK tree structures are similar to the BDT tree structure.
You can see this if you examine the sample HW tree contained in the
file `deriv.mat`

.

```
load deriv.mat;
HWTree
```

HWTree = FinObj: 'HWFwdTree' VolSpec: [1x1 struct] TimeSpec: [1x1 struct] RateSpec: [1x1 struct] tObs: [0 1.00 2.00 3.00] dObs: [731947.00 732313.00 732678.00 733043.00] CFlowT: {[4x1 double] [3x1 double] [2x1 double] [4.00]} Probs: {[3x1 double] [3x3 double] [3x5 double]} Connect: {[2.00] [2.00 3.00 4.00] [2.00 2.00 3.00 4.00 4.00]} FwdTree: {[1.03] [1.05 1.04 1.02] [1.08 1.07 1.05 1.03 1.01] [1.09 1.08 1.06 1.04 1.02]

All fields of this structure are similar to their BDT counterparts.
There are two additional fields not present in BDT: `Probs`

and `Connect`

.
The `Probs`

field represents the occurrence probabilities
at each branch of each node in the tree. The `Connect`

field
describes the connectivity of the nodes of a given tree level to nodes
to the next tree level.

** Probs Field. **While BDT and one-factor HJM models have equal probabilities for each
branch at a node, HW and BK do not. For HW and BK trees, the

`Probs`

field indicates the likelihood that a
particular branch will be taken in moving from one node to another node on
the next level.The `Probs`

field consists of a cell array with one cell
per tree level. Each cell is a
`3`

-by-`NUMNODES`

array with the top
row representing the probability of an up movement, the middle row
representing the probability of a middle movement, and the last row the
probability of a down movement.

As an illustration, consider the first two elements of the
`Probs`

field of the structure, corresponding to the
first (root) and second levels of the tree.

HWTree.Probs{1}

0.16666666666667 0.66666666666667 0.16666666666667

HWTree.Probs{2}

0.12361333418768 0.16666666666667 0.21877591615172 0.65761074966060 0.66666666666667 0.65761074966060 0.21877591615172 0.16666666666667 0.12361333418768

Reading from top to bottom, the values in
`HWTree.Probs{1}`

correspond to the up, middle, and
down probabilities at the root node.

`HWTree.Probs{2}`

is a
`3`

-by-`3`

matrix of values. The first
column represents the top node, the second column represents the middle
node, and the last column represents the bottom node. As with the root node,
the first, second, and third rows hold the values for up, middle, and down
branching off each node.

As expected, the sum of all the probabilities at any node equals 1.

sum(HWTree.Probs{2})

1.0000 1.0000 1.0000

**Connect Field. **The other field that distinguishes HW and BK tree structures
from the BDT tree structure is `Connect`

. This field
describes how each node in a given level connects to the nodes of
the next level. The need for this field arises from the possibility
of nonstandard branching in a tree.

The `Connect`

field of the HW tree structure consists of a cell array with
one cell per tree level.

HWTree.Connect

ans = [2] [1x3 double] [1x5 double]

Each cell contains a `1`

-by-`NUMNODES`

vector.
Each value in the vector relates to a node in the corresponding tree
level and represents the index of the node in the next tree level
that the middle branch of the node connects to.

If you subtract 1 from the values contained in `Connect`

,
you reveal the index of the nodes in the next level that the up branch
connects to. If you add `1`

to the values, you reveal
the index of the corresponding down branch.

As an illustration, consider `HWTree.Connect{1}`

:

HWTree.Connect{1}

ans = 2

This indicates that the middle branch of the root node connects
to the second (from the top) node of the next level, as expected.
If you subtract `1`

from this value, you obtain `1`

,
which tells you that the up branch goes to the top node. If you add `1`

,
you obtain `3`

, which points to the last node of
the second level of the tree.

Now consider level 3 in this example:

HWTree.Connect{3}

2 2 3 4 4

On this level, there is nonstandard branching. This can be easily recognized because the middle branch of two nodes is connected to the same node on the next level.

To visualize this, consider the following illustration of the tree.

Here it becomes apparent that there is nonstandard branching at the third level of the tree, on the top and bottom nodes. The first and second nodes connect to the same trio of nodes on the next level. Similar branching occurs at the bottom and next-to-bottom nodes of the tree.

`bdtprice`

| `bdtsens`

| `bdttimespec`

| `bdttree`

| `bdtvolspec`

| `bkprice`

| `bksens`

| `bktimespec`

| `bktree`

| `bkvolspec`

| `bondbybdt`

| `bondbybk`

| `bondbyhjm`

| `bondbyhw`

| `bondbyzero`

| `capbybdt`

| `capbybk`

| `capbyblk`

| `capbyhjm`

| `capbyhw`

| `cfbybdt`

| `cfbybk`

| `cfbyhjm`

| `cfbyhw`

| `cfbyzero`

| `fixedbybdt`

| `fixedbybk`

| `fixedbyhjm`

| `fixedbyhw`

| `fixedbyzero`

| `floatbybdt`

| `floatbybk`

| `floatbyhjm`

| `floatbyhw`

| `floatbyzero`

| `floatdiscmargin`

| `floatmargin`

| `floorbybdt`

| `floorbybk`

| `floorbyblk`

| `floorbyhjm`

| `floorbyhw`

| `hjmprice`

| `hjmsens`

| `hjmtimespec`

| `hjmtree`

| `hjmvolspec`

| `hwcalbycap`

| `hwcalbyfloor`

| `hwprice`

| `hwsens`

| `hwtimespec`

| `hwtree`

| `hwvolspec`

| `instbond`

| `instcap`

| `instcf`

| `instfixed`

| `instfloat`

| `instfloor`

| `instoptbnd`

| `instoptembnd`

| `instoptemfloat`

| `instoptfloat`

| `instrangefloat`

| `instswap`

| `instswaption`

| `intenvprice`

| `intenvsens`

| `intenvset`

| `mmktbybdt`

| `mmktbyhjm`

| `oasbybdt`

| `oasbybk`

| `oasbyhjm`

| `oasbyhw`

| `optbndbybdt`

| `optbndbybk`

| `optbndbyhjm`

| `optbndbyhw`

| `optembndbybdt`

| `optembndbybk`

| `optembndbyhjm`

| `optembndbyhw`

| `optemfloatbybdt`

| `optemfloatbybk`

| `optemfloatbyhjm`

| `optemfloatbyhw`

| `optfloatbybdt`

| `optfloatbybk`

| `optfloatbyhjm`

| `optfloatbyhw`

| `rangefloatbybdt`

| `rangefloatbybk`

| `rangefloatbyhjm`

| `rangefloatbyhw`

| `swapbybdt`

| `swapbybk`

| `swapbyhjm`

| `swapbyhw`

| `swapbyzero`

| `swaptionbybdt`

| `swaptionbybk`

| `swaptionbyblk`

| `swaptionbyhjm`

| `swaptionbyhw`

- Overview of Interest-Rate Tree Models
- Pricing Using Interest-Rate Term Structure
- Pricing Using Interest-Rate Tree Models
- Graphical Representation of Trees