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Financial Instruments Toolbox™ software offers two functions
for assessing the fundamental hedging tradeoff, `hedgeopt` and `hedgeslf`.

The first function, `hedgeopt`,
addresses the most general hedging problem. It allocates an optimal
hedge to satisfy either of two goals:

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

Minimize portfolio sensitivities for a given set of maximum target costs.

`hedgeopt` allows investors
to modify portfolio allocations among instruments according to either
of the goals. The problem is cast as a constrained linear least-squares
problem. For additional information about `hedgeopt`,
see Hedging with hedgeopt.

The second function, `hedgeslf`,
attempts to allocate a self-financing hedge among a portfolio of instruments.
In particular, `hedgeslf` attempts
to maintain a constant portfolio value consistent with reduced portfolio
sensitivities (that is, the rebalanced portfolio is hedged against
market moves and is closest to being self-financing). If `hedgeslf` cannot find a self-financing
hedge, it rebalances the portfolio to minimize overall portfolio sensitivities.
For additional information on `hedgeslf`,
see Self-Financing Hedges with hedgeslf.

The examples in this section consider the *delta*, *gamma*,
and *vega* sensitivity measures. In this toolbox,
when you work with *interest-rate derivatives*, delta
is the price sensitivity measure of shifts in the forward yield curve, gamma
is the delta sensitivity measure of shifts in the forward yield curve,
and vega is
the price sensitivity measure of shifts in the volatility process.
See `bdtsens` or `hjmsens` for details on the computation
of sensitivities for interest-rate derivatives.

For *equity exotic options*, the underlying
instrument is the stock price instead of the forward yield curve.
Consequently, delta now represents the price sensitivity measure of
shifts in the stock price, gamma is the delta sensitivity measure
of shifts in the stock price, and vega is the price sensitivity measure
of shifts in the volatility of the stock. See `crrsens`, `eqpsens`, or `ittsens` for
details on the computation of sensitivities for equity derivatives.

For examples showing the computation of sensitivities for interest-rate based derivatives, see Computing Instrument Sensitivities. Likewise, for examples showing the computation of sensitivities for equity exotic options, see Computing Instrument Sensitivities.

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.

Both hedging functions require some common inputs, including the current portfolio holdings (allocations), and a matrix of instrument sensitivities. To create these inputs, load the example portfolio into memory

```
load deriv.mat;
```

compute price and sensitivities

[Delta, Gamma, Vega, Price] = hjmsens(HJMTree, HJMInstSet);

Warning: Not all cash flows are aligned with the tree. Result will be approximated.

and extract the current portfolio holdings.

Holdings = instget(HJMInstSet, 'FieldName', 'Quantity');

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.

To summarize the portfolio information

disp([Price Holdings Sensitivities])

98.72 100.00 -272.65 1029.90 0.00 97.53 50.00 -347.43 1622.69 -0.04 0.05 -50.00 -8.08 643.40 34.07 98.72 80.00 -272.65 1029.90 0.00 100.55 8.00 -1.04 3.31 0 6.28 30.00 294.97 6852.56 93.69 0.05 40.00 -47.16 8459.99 93.69 3.69 10.00 -282.05 1059.68 0.00

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 = -61910.22 788946.21 4852.91

To illustrate using `hedgeopt`,
suppose that you want to maintain your existing portfolio. The first
form of `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.

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

Sens = -61910.22 788946.21 4852.91 Cost = 0 Quantity' = 100.00 50.00 -50.00 80.00 8.00 30.00 40.00 10.00

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 = 23674.62

Value1 = Quantity * Price

Value1 = 23674.62

Building on the example in Maintaining Existing Allocations, 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];

Then, specify the instruments to be fixed.

FixedInd = [1 4 5 7 8];

Finally, call `hedgeopt`

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

and again examine the results.

Sens = -23000.00 -3300.00 3000.00 Cost = 19174.02 Quantity' = 100.00 -141.03 137.26 80.00 8.00 -57.96 40.00 10.00

Recompute `Value1`, the portfolio value after
rebalancing.

Value1 = Quantity * Price

Value1 = 4500.60

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 have been changed.

The example in Partially Hedged Portfolio illustrates 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 0s and
call `hedgeopt` again. The following
example uses data from Hedging with hedgeopt.

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

Examining the outputs reveals that you have obtained a fully hedged portfolio

Sens = -0.00 -0.00 -0.00

but at an expense of over $20,000.

Cost = 23055.90

The positions required to achieve a fully hedged portfolio

Quantity' = 100.00 -182.36 -19.55 80.00 8.00 -32.97 40.00 10.00

result in the new portfolio value

Value1 = Quantity * Price

Value1 = 618.72

The examples in Fully Hedged Portfolio illustrate how to use `hedgeopt` to determine the minimum cost
of hedging a portfolio given a set of target sensitivities. In these
examples, portfolio target sensitivities are treated as equality constraints
during the optimization process. You tell `hedgeopt` what
sensitivities you want, and it tells you what it will cost to get
those sensitivities.

A related problem involves minimizing portfolio sensitivities
for a given set of maximum target costs. For this goal, the target
costs are treated as inequality constraints during the optimization
process. You tell `hedgeopt` the
most you are willing spend to insulate your portfolio, and it tells
you the smallest portfolio sensitivities you can get for your money.

To illustrate this use of `hedgeopt`,
compute the portfolio dollar sensitivities along the entire cost frontier.
From the previous examples, you know that spending nothing replicates
the existing portfolio, while spending $23,055.90 completely hedges
the portfolio.

Assume, for example, you are willing to spend as much as $50,000, and want to see what portfolio sensitivities will result along the cost frontier. Assume that the same instruments are held fixed, and that the cost frontier is evaluated from $0 to $50,000 at increments of $1000.

MaxCost = [0:1000:50000];

Now, call `hedgeopt`.

[Sens, Cost, Quantity] = hedgeopt(Sensitivities, Price, ... Holdings, FixedInd, [], MaxCost);

With this data, you can plot the required hedging cost versus the funds available (the amount you are willing to spend)

plot(MaxCost/1000, Cost/1000, 'red'), grid xlabel('Funds Available for Rebalancing ($1000''s)') ylabel('Actual Rebalancing Cost ($1000''s)') title ('Rebalancing Cost Profile')

**Rebalancing Cost Profile**

and the portfolio dollar sensitivities versus the funds available.

figure plot(MaxCost/1000, Sens(:,1), '-red') hold('on') plot(MaxCost/1000, Sens(:,2), '-.black') plot(MaxCost/1000, Sens(:,3), '--blue') grid xlabel('Funds Available for Rebalancing ($1000''s)') ylabel('Delta, Gamma, and Vega Portfolio Dollar Sensitivities') title ('Portfolio Sensitivities Profile') legend('Delta', 'Gamma', 'Vega', 0)

**Funds Available for Rebalancing**

The figures Rebalancing Cost Profile and Funds Available for Rebalancing indicate that there is no
benefit because the funds available for hedging exceed $23,055.90,
the point of maximum expense required to obtain simultaneous delta,
gamma, and vega neutrality. You can also find this point of delta,
gamma, and vega neutrality using `hedgeslf`.

[Sens, Value1, Quantity] = hedgeslf(Sensitivities, Price,... Holdings, FixedInd);

Sens = -0.00 -0.00 -0.00 Value1 = 618.72 Quantity = 100.00 -182.36 -19.55 80.00 8.00 -32.97 40.00 10.00

Similar to `hedgeopt`, `hedgeslf` returns the portfolio dollar
sensitivities and instrument quantities (the rebalanced holdings).
However, in contrast, the second output parameter of `hedgeslf` is the value of the rebalanced
portfolio, from which you can calculate the rebalancing cost by subtraction.

Value0 - Value1

ans = 23055.90

In this example, the portfolio is clearly not self-financing,
so `hedgeslf` finds the best
possible solution required to obtain zero sensitivities.

There is, in fact, a third calling syntax available for `hedgeopt` directly related to the results
shown above for `hedgeslf`. Suppose,
instead of directly specifying the funds available for rebalancing
(the most money you are willing to spend), you want to simply specify
the number of points along the cost frontier. This call to `hedgeopt` samples the cost frontier at
10 equally spaced points between the point of minimum cost (and potentially
maximum exposure) and the point of minimum exposure (and maximum cost).

[Sens, Cost, Quantity] = hedgeopt(Sensitivities, Price,... Holdings, FixedInd, 10);

Sens = -32784.46 2231.83 -49694.33 -29141.74 1983.85 -44172.74 -25499.02 1735.87 -38651.14 -21856.30 1487.89 -33129.55 -18213.59 1239.91 -27607.96 -14570.87 991.93 -22086.37 -10928.15 743.94 -16564.78 -7285.43 495.96 -11043.18 -3642.72 247.98 -5521.59 0.00 -0.00 0.00 Cost = 0.00 2561.77 5123.53 7685.30 10247.07 12808.83 15370.60 17932.37 20494.14 23055.90

Now plot this data.

figure plot(Cost/1000, Sens(:,1), '-red') hold('on') plot(Cost/1000, Sens(:,2), '-.black') plot(Cost/1000, Sens(:,3), '--blue') grid xlabel('Rebalancing Cost ($1000''s)') ylabel('Delta, Gamma, and Vega Portfolio Dollar Sensitivities') title ('Portfolio Sensitivities Profile') legend('Delta', 'Gamma', 'Vega', 0)

**Rebalancing Cost**

In this calling form, `hedgeopt` calls `hedgeslf` internally to determine the
maximum cost needed to minimize the portfolio sensitivities ($23,055.90),
and evenly samples the cost frontier between $0 and $23,055.90.

Note that both `hedgeopt` and `hedgeslf` cast the optimization problem
as a constrained linear least squares problem. Depending on the instruments
and constraints, neither function is guaranteed to converge to a solution.
In some cases, the problem space may be unbounded, and additional
instrument equality constraints, or user-specified constraints, may
be necessary for convergence. See Hedging with Constrained Portfolios for
additional information.

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