# hedgeopt

Allocate optimal hedge for target costs or sensitivities

## Syntax

## Description

`[`

allocates an optimal hedge by one of two criteria:`PortSens`

,`PortCost`

,`PortHolds`

] = hedgeopt(`Sensitivities`

,`Price`

,`CurrentHolds`

)

Minimize portfolio sensitivities (exposure) for a given set of target costs.

Minimize the cost of hedging a portfolio given a set of target sensitivities.

Hedging involves the fundamental tradeoff between portfolio insurance and the cost of insurance coverage. This function lets investors modify portfolio allocations among instruments to achieve either of the criteria. The chosen criterion is inferred from the input argument list. The problem is cast as a constrained linear least-squares problem.

`[`

adds additional optional arguments.`PortSens`

,`PortCost`

,`PortHolds`

] = hedgeopt(___,`FixedInd`

,`NumCosts`

,`TargetCost`

,`TargetSens`

`ConSet`

)

## Examples

### Allocate Optimal Hedge for Target Costs

To illustrate the hedging facility, consider the portfolio `HJMInstSet`

obtained from the example file `deriv.mat`

. The portfolio consists of eight instruments: two bonds, one bond option, one fixed-rate note, one floating-rate note, one cap, one floor, and one swap.

In this examples, portfolio target sensitivities are treated as equality constraints during the optimization process. You can use `hedgeopt`

to specify what sensitivities you want, and `hedgeopt`

computes what it will cost to get those sensitivities.

`load deriv.mat;`

Compute the price and sensitivities

```
warning('off')
[Delta, Gamma, Vega, Price] = hjmsens(HJMTree, HJMInstSet)
```

`Delta = `*8×1*
-272.6462
-347.4315
-8.0781
-272.6462
-1.0445
294.9700
-47.1629
-282.0465

Gamma =8×110^{3}× 1.0299 1.6227 0.6434 1.0299 0.0033 6.8526 8.4600 1.0597

`Vega = `*8×1*
0.0000
-0.0397
34.0746
0.0000
0
93.6946
93.6946
0.0000

`Price = `*8×1*
98.7159
97.5280
0.0486
98.7159
100.5529
6.2831
0.0486
3.6923

Extract the current portfolio holdings.

warning('on') Holdings = instget(HJMInstSet, 'FieldName', 'Quantity')

`Holdings = `*8×1*
100
50
-50
80
8
30
40
10

For convenience place the `delta`

, `gamma`

, and `vega`

sensitivity measures into a matrix of sensitivities.

Sensitivities = [Delta Gamma Vega];

Each row of the `Sensitivities`

matrix is associated with a different instrument in the portfolio, and each column with a different sensitivity measure.

Summarize the portfolio information.

disp([Price Holdings Sensitivities])

1.0e+03 * 0.0987 0.1000 -0.2726 1.0299 0.0000 0.0975 0.0500 -0.3474 1.6227 -0.0000 0.0000 -0.0500 -0.0081 0.6434 0.0341 0.0987 0.0800 -0.2726 1.0299 0.0000 0.1006 0.0080 -0.0010 0.0033 0 0.0063 0.0300 0.2950 6.8526 0.0937 0.0000 0.0400 -0.0472 8.4600 0.0937 0.0037 0.0100 -0.2820 1.0597 0.0000

The first column above is the dollar unit price of each instrument, the second is the holdings of each instrument (the quantity held or the number of contracts), and the third, fourth, and fifth columns are the dollar `delta`

, `gamma`

, and `vega`

sensitivities, respectively.

The current portfolio sensitivities are a weighted average of the instruments in the portfolio.

TargetSens = Holdings' * Sensitivities

TargetSens =1×310^{5}× -0.6191 7.8895 0.0485

**Maintaining Existing Allocations**

To illustrate using `hedgeopt`

, suppose that you want to maintain your existing portfolio. `hedgeopt`

minimizes the cost of hedging a portfolio given a set of target sensitivities. If you want to maintain your existing portfolio composition and exposure, you should be able to do so without spending any money. To verify this, set the target sensitivities to the current sensitivities.

FixedInd = [1 2 3 4 5 6 7 8]; [Sens, Cost, Quantity] = hedgeopt(Sensitivities, Price,Holdings, FixedInd, [], [], TargetSens)

Sens =1×310^{5}× -0.6191 7.8895 0.0485

Cost = 0

`Quantity = `*1×8*
100 50 -50 80 8 30 40 10

Portfolio composition and sensitivities are unchanged, and the cost associated with doing nothing is zero. The cost is defined as the change in portfolio value. This number cannot be less than zero because the rebalancing cost is defined as a nonnegative number.

If `Value0`

and `Value1`

represent the portfolio value before and after rebalancing, respectively, the zero cost can also be verified by comparing the portfolio values.

Value0 = Holdings' * Price

Value0 = 2.3675e+04

Value1 = Quantity * Price

Value1 = 2.3675e+04

**Partially Hedged Portfolio**

Building on this example, suppose you want to know the cost to achieve an overall portfolio dollar sensitivity of `[-23000 -3300 3000]`

, while allowing trading only in instruments `2`

, `3`

, and `6`

(holding the positions of instruments `1`

, `4`

, `5`

, `7`

, and `8`

fixed). To find the cost, first set the target portfolio dollar sensitivity.

TargetSens = [-23000 -3300 3000];

Specify the instruments to be fixed.

FixedInd = [1 4 5 7 8];

Use `hedgeopt:`

[Sens, Cost, Quantity] = hedgeopt(Sensitivities, Price,Holdings, FixedInd, [], [], TargetSens)

Sens =1×310^{4}× -2.3000 -0.3300 0.3000

Cost = 1.9174e+04

`Quantity = `*1×8*
100.0000 -141.0267 137.2638 80.0000 8.0000 -57.9606 40.0000 10.0000

Recompute `Value1`

, the portfolio value after rebalancing.

Value1 = Quantity * Price

Value1 = 4.5006e+03

As expected, the cost, $19174.02, is the difference between `Value0`

and `Value1`

, $23674.62 — $4500.60. Only the positions in instruments `2`

, `3`

, and `6`

are changed.

**Fully Hedged Portfolio**

The example has illustrated a partial hedge, but perhaps the most interesting case involves the cost associated with a fully hedged portfolio (simultaneous `delta`

, `gamma`

, and `vega`

neutrality). In this case, set the target sensitivity to a row vector of `0`

s and call `hedgeopt`

again.

TargetSens = [0 0 0]; [Sens, Cost, Quantity] = hedgeopt(Sensitivities, Price, Holdings, FixedInd, [], [], TargetSens)

Sens =1×310^{-10}× 0.1091 0.5821 0.0045

Cost = 2.3056e+04

`Quantity = `*1×8*
100.0000 -182.3615 -19.5501 80.0000 8.0000 -32.9674 40.0000 10.0000

Examining the outputs reveals that you have obtained a fully hedged portfolio but at an expense of over $20,000 and `Quantity`

defines the positions required to achieve a fully hedged portfolio.

The resulting new portfolio value is

Value1 = Quantity * Price

Value1 = 618.7168

## Input Arguments

`Sensitivities`

— Sensitivities of each instrument

matrix

Sensitivities of each instrument, specified as a number of instruments
(`NINST`

) by number of sensitivities (`NSENS`

)
matrix of dollar sensitivities. Each row represents a different instrument. Each column
represents a different sensitivity.

**Data Types: **`double`

`Price`

— Instrument prices

vector

Instrument prices, specified as an `NINST`

-by-`1`

vector.

**Data Types: **`double`

`CurrentHolds`

— Contracts allocated to each instrument

vector

Contracts allocated to each instrument, specified as an
`NINST`

-by-`1`

vector.

**Data Types: **`double`

`FixedInd`

— Number of fixed instruments

`[ ]`

(default) | vector

(Optional) Number of fixed instruments, specified as an
`NFIXED`

-by-`1`

vector of indices of instruments to
hold fixed. For example, to hold the first and third instruments of a 10 instrument
portfolio unchanged, set `FixedInd = [1 3]`

. Default =
`[]`

, no instruments held fixed.

**Data Types: **`double`

`NumCosts`

— Number of points generated along the cost frontier

`10`

(default) | integer

(Optional) Number of points generated along the cost frontier when a vector of
target costs (`TargetCost`

) is not defined, specified as an integer.
The default is 10 equally spaced points between the point of minimum cost and the point
of minimum exposure. When specifying `TargetCost`

, enter
`NumCosts`

as an empty matrix `[]`

.

**Data Types: **`double`

`TargetCost`

— Target cost values along the cost frontier

`[ ]`

(default) | vector

(Optional) Target cost values along the cost frontier, specified as a vector. If
`TargetCost`

is empty, or not entered, `hedgeopt`

evaluates `NumCosts`

equally spaced target costs between the minimum
cost and minimum exposure. When specified, the elements of `TargetCost`

should be positive numbers that represent the maximum amount of money the owner is
willing to spend to rebalance the portfolio.

**Data Types: **`double`

`TargetSens`

— Target sensitivity values of the portfolio

`[ ]`

(default) | vector

(Optional) Target sensitivity values of the portfolio, specified as a
`1`

-by-`NSENS`

vector containing the target
sensitivity values of the portfolio. When specifying `TargetSens`

,
enter `NumCosts`

and `TargetCost`

as empty
matrices `[]`

.

**Data Types: **`double`

`ConSet`

— Additional conditions on the portfolio reallocations

`[ ]`

(default) | matrix

(Optional) Additional conditions on the portfolio reallocations, specified as a
number of constraints (`NCONS`

) by number of instruments
(`NINST`

) matrix of additional conditions on the portfolio
reallocations. An eligible `NINST`

-by-`1`

vector of
contract holdings, `PortWts`

, satisfies all the inequalities
`A*PortWts <= b`

, where `A = ConSet(:,1:end-1)`

and `b = ConSet(:,end)`

.

**Note**

The user-specified constraints included in `ConSet`

may be
created with the functions `pcalims`

or `portcons`

. However, the `portcons`

default `PortHolds`

positivity constraints
are typically inappropriate for hedging problems since short-selling is usually
required.

`NPOINTS`

, the number of rows in `PortSens`

and `PortHolds`

and the length of `PortCost`

,
is inferred from the inputs. When the target sensitivities,
`TargetSens`

, is entered, `NPOINTS = 1`

;
otherwise `NPOINTS = NumCosts`

, or is equal to the length of the
`TargetCost`

vector.

Not all problems are solvable (for example, the solution space may be infeasible
or unbounded, or the solution may fail to converge). When a valid solution is not
found, the corresponding rows of `PortSens`

,
`PortHolds`

, and the elements of `PortCost`

are padded with `NaN`

s as placeholders.

**Data Types: **`double`

## Output Arguments

`PortSens`

— Portfolio dollar sensitivities

matrix

Portfolio dollar sensitivities, returned as a number of points
(`NPOINTS`

-by-`NSENS`

) matrix. When a perfect hedge
exists, `PortSens`

is zeros. Otherwise, the best hedge possible is
chosen.

**Note**

Not all problems are solvable (for example, the solution space may be
infeasible, unbounded, or insufficiently constrained), or the solution may fail to
converge. When a valid solution is not found, the corresponding rows of
`PortSens`

and `PortHolds`

and elements of
`PortCost`

are padded with `NaN`

's as
placeholders. In addition, the solution may not be unique.

`PortCost`

— Total portfolio costs

vector

Total portfolio costs, returned as a
`1`

-by-`NPOINTS`

vector.

`PortHolds`

— Contracts allocated to each instrument

matrix

Contracts allocated to each instrument, returned as an
`NPOINTS`

-by-`NINST`

matrix. These are the
reallocated portfolios.

## Version History

**Introduced before R2006a**

## Apri esempio

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