# hedgeslf

Self-financing hedge

## Syntax

## Description

`[`

allocates a self-financing hedge among a collection of instruments.
`PortSens`

,`PortValue`

,`PortHolds`

] = hedgeslf(`Sensitivities`

,`Price`

,`CurrentHolds`

)`hedgeslf`

finds the reallocation in a portfolio of financial
instruments that hedges the portfolio against market moves and that is closest to
being self-financing (maintaining constant portfolio value). By default the first
instrument entered is hedged with the other instruments.

`hedgeslf`

attempts to find the allocations of the portfolio that
will make it closest to being self-financing, while reducing the sensitivities to
zero. If no solution is found, `hedgeslf`

finds the allocations
that will minimize the sensitivities. If the resulting portfolio is self-financing,
`PortValue`

is equal to the value of the original
portfolio.

## Examples

### Pricing and Hedging a Portfolio Using the Black-Karasinski Model

This example illustrates how MATLAB® can be used to create a portfolio of interest-rate derivatives securities, and price it using the Black-Karasinski interest-rate model. The example also shows some hedging strategies to minimize exposure to market movements.

**Create the Interest-Rate Term Structure Based on Reported Data**

The structure `RateSpec`

is an interest-rate term structure that defines the initial rate specification from which the tree rates are derived. Use the information of annualized zero coupon rates in the table below to populate the `RateSpec`

structure.

From To Rate 27 Feb 2007 27 Feb 2008 0.0493 27 Feb 2007 27 Feb 2009 0.0459 27 Feb 2007 27 Feb 2010 0.0450 27 Feb 2007 27 Feb 2012 0.0446 27 Feb 2007 27 Feb 2014 0.0445 27 Feb 2007 27 Feb 2017 0.0450 27 Feb 2007 27 Feb 2027 0.0473

This data could be retrieved from the Federal Reserve Statistical Release page by using the Datafeed Toolbox™. In this case, the Datafeed Toolbox™ will connect to FRED® and pull back the rates of the following treasury notes.

Terms Symbol ======= ====== 1 = DGS1 2 = DGS2 3 = DGS3 5 = DGS5 7 = DGS7 10 = DGS10 20 = DGS20

Create the connection object:

c = fred;

Create the symbol fetch list:

FredNames = { ... 'DGS1'; ... % 1 Year 'DGS2'; ... % 2 Year 'DGS3'; ... % 3 Year 'DGS5'; ... % 5 Year 'DGS7'; ... % 7 Year 'DGS10'; ... % 10 Year 'DGS20'}; % 20 Year

Define the Terms:

Terms = [ 1; ... % 1 Year 2; ... % 2 Year 3; ... % 3 Year 5; ... % 5 Year 7; ... % 7 Year 10; ... % 10 Year 20]; % 20 Year

Set the `StartDate`

to Feb 27, 2007:

StartDate = datenum('Feb-27-2007');

FredRet = fetch(c,FredNames,StartDate);

Set the `ValuationDate`

based on the `StartDate`

:

ValuationDate = StartDate;

EndDates = [];

Rates =[];

Create the `EndDates`

:

for idx = 1:length(FredRet)

%Pull the rates associated with Feb 27, 2007. All the Fred Rates come %back as percents Rates = [Rates; ... FredRet(idx).Data(1,2) / 100];

%Determine the EndDates by adding the Term to the year of the %StartDate EndDates = [EndDates; ... round(datenum(... year(StartDate)+ Terms(idx,1), ... month(StartDate),... day(StartDate)))];

end

Use the function `intenvset`

to create the `RateSpec`

with the following data:

Compounding = 1; StartDate = '27-Feb-2007'; Rates = [0.0493; 0.0459; 0.0450; 0.0446; 0.0446; 0.0450; 0.0473]; EndDates = {'27-Feb-2008'; '27-Feb-2009';'27-Feb-2010'; '27-Feb-2012';... '27-Feb-2014' ; '27-Feb-2017'; '27-Feb-2027'}; ValuationDate = StartDate; RateSpec = intenvset('Compounding',Compounding,'StartDates', StartDate,... 'EndDates', EndDates, 'Rates', Rates,'ValuationDate', ValuationDate)

`RateSpec = `*struct with fields:*
FinObj: 'RateSpec'
Compounding: 1
Disc: [7x1 double]
Rates: [7x1 double]
EndTimes: [7x1 double]
StartTimes: [7x1 double]
EndDates: [7x1 double]
StartDates: 733100
ValuationDate: 733100
Basis: 0
EndMonthRule: 1

**Specify the Volatility Model**

Create the structure `VolSpec`

that specifies the volatility process with the following data.

Volatility = [0.011892; 0.01563; 0.02021; 0.02125; 0.02165; 0.02065; 0.01803]; Alpha = [0.0001]; VolSpec = bkvolspec(ValuationDate, EndDates, Volatility, EndDates(end), Alpha)

`VolSpec = `*struct with fields:*
FinObj: 'BKVolSpec'
ValuationDate: 733100
VolDates: [7x1 double]
VolCurve: [7x1 double]
AlphaCurve: 1.0000e-04
AlphaDates: 740405
VolInterpMethod: 'linear'

**Specify the Time Structure of the Tree**

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 = bktimespec(ValuationDate, EndDates)

`TimeSpec = `*struct with fields:*
FinObj: 'BKTimeSpec'
ValuationDate: 733100
Maturity: [7x1 double]
Compounding: -1
Basis: 0
EndMonthRule: 1

**Create the BK Tree**

Use the previously computed values for `RateSpec`

, `VolSpec`

, and `TimeSpec`

to create the BK tree.

BKTree = bktree(VolSpec, RateSpec, TimeSpec)

`BKTree = `*struct with fields:*
FinObj: 'BKFwdTree'
VolSpec: [1x1 struct]
TimeSpec: [1x1 struct]
RateSpec: [1x1 struct]
tObs: [0 1 2 3 5 7 10]
dObs: [733100 733465 733831 734196 734926 735657 736753]
CFlowT: {1x7 cell}
Probs: {1x6 cell}
Connect: {[2] [2 3 4] [2 3 4 5 6] [2 3 3 4 5 5 6] [2 3 4 5 6 7 8] [2 2 ... ]}
FwdTree: {1x7 cell}

Visualize the interest rate evolution along the tree by looking at the output structure `BKTree`

. The function `bktree`

returns an inverse discount tree, which you can convert into an interest rate tree with the `cvtree`

function.

BKTreeR = cvtree(BKTree)

`BKTreeR = `*struct with fields:*
FinObj: 'BKRateTree'
VolSpec: [1x1 struct]
TimeSpec: [1x1 struct]
RateSpec: [1x1 struct]
tObs: [0 1 2 3 5 7 10]
dObs: [733100 733465 733831 734196 734926 735657 736753]
CFlowT: {1x7 cell}
Probs: {1x6 cell}
Connect: {[2] [2 3 4] [2 3 4 5 6] [2 3 3 4 5 5 6] [2 3 4 5 6 7 8] [2 2 ... ]}
RateTree: {1x7 cell}

Look at the upper, middle and lower branch paths of the tree.

OldFormat = get(0, 'format'); format short %Rate at root node: RateRoot = trintreepath(BKTreeR, 0)

RateRoot = 0.0481

```
%Rates along upper branch:
RatePathUp = trintreepath(BKTreeR, [1 1 1 1 1 1])
```

`RatePathUp = `*7×1*
0.0481
0.0425
0.0446
0.0478
0.0510
0.0555
0.0620

```
%Rates along middle branch:
RatePathMiddle = trintreepath(BKTreeR, [2 2 2 2 2 2])
```

`RatePathMiddle = `*7×1*
0.0481
0.0416
0.0423
0.0430
0.0436
0.0449
0.0484

```
%Rates along lower branch:
RatePathDown = trintreepath(BKTreeR, [3 3 3 3 3 3])
```

`RatePathDown = `*7×1*
0.0481
0.0408
0.0401
0.0388
0.0373
0.0363
0.0378

You can also display a graphical representation of the tree to examine interactively the rates on the nodes of the tree until maturity. The function `treeviewer`

displays the structure of the rate tree in the left window. The tree visualization in the right window is blank, but by selecting **Table**/**Diagram** and clicking on the nodes you can examine the rates along the paths.

treeviewer(BKTreeR);

**Create an Instrument Portfolio**

Create a portfolio consisting of two bonds instruments and an option on the 5% bond.

% Two Bonds CouponRate = [0.04;0.05]; Settle = '27 Feb 2007'; Maturity = {'27 Feb 2009';'27 Feb 2010'}; Period = 1; % American Option on the 5% Bond OptSpec = {'call'}; Strike = 98; ExerciseDates = '27 Feb 2010'; AmericanOpt = 1; InstSet = instadd('Bond', CouponRate, Settle, Maturity, Period); InstSet = instadd(InstSet,'OptBond', 2, OptSpec, Strike, ExerciseDates, AmericanOpt); % Assign Names and Holdings Holdings = [10; 15;3]; Names = {'4% Bond'; '5% Bond'; 'Option 98'}; InstSet = instsetfield(InstSet, 'Index',1:3, 'FieldName', {'Quantity'}, 'Data', Holdings ); InstSet = instsetfield(InstSet, 'Index',1:3, 'FieldName', {'Name'}, 'Data', Names );

Examine the set of instruments contained in the variable `InstSet`

.

instdisp(InstSet)

Index Type CouponRate Settle Maturity Period Basis EndMonthRule IssueDate FirstCouponDate LastCouponDate StartDate Face Quantity Name 1 Bond 0.04 27-Feb-2007 27-Feb-2009 1 0 1 NaN NaN NaN NaN 100 10 4% Bond 2 Bond 0.05 27-Feb-2007 27-Feb-2010 1 0 1 NaN NaN NaN NaN 100 15 5% Bond Index Type UnderInd OptSpec Strike ExerciseDates AmericanOpt Quantity Name 3 OptBond 2 call 98 27-Feb-2010 1 3 Option 98

**Price the Portfolio Using the BK Model**

Calculate the price of each instrument in the portfolio.

[Price, PTree] = bkprice(BKTree, InstSet)

`Price = `*3×1*
98.8841
101.3470
3.3470

`PTree = `*struct with fields:*
FinObj: 'BKPriceTree'
PTree: {1x8 cell}
AITree: {1x8 cell}
ExTree: {1x8 cell}
tObs: [0 1 2 3 5 7 10 20]
Connect: {[2] [2 3 4] [2 3 4 5 6] [2 3 3 4 5 5 6] [2 3 4 5 6 7 8] [2 2 3 ... ]}
Probs: {1x6 cell}

The prices in the output vector `Price`

correspond to the prices at observation time zero (`tObs`

= `0`

), which is defined as the Valuation Date of the interest-rate tree.

In the `Price`

vector, the first element, 98.884, represents the price of the first instrument (4% Bond); the second element, 101.347, represents the price of the second instrument (5% Bond), and 3.347 represents the price of the American call option.

You can also display a graphical representation of the price tree to examine the prices on the nodes of the tree until maturity.

treeviewer(PTree,InstSet);

**Add More Instruments to the Existing Portfolio**

Add instruments to the existing portfolio: cap, floor, floating rate note, vanilla swap and a puttable and callable bond.

% Cap StrikeC =0.035; InstSet = instadd(InstSet,'Cap', StrikeC, Settle, '27 Feb 2010'); % Floor StrikeF =0.05; InstSet = instadd(InstSet,'Floor', StrikeF, Settle, '27 Feb 2009'); % Floating Rate Note InstSet = instadd(InstSet,'Float', 30, Settle, '27 Feb 2009'); % Vanilla Swap LegRate =[0.04 5]; InstSet = instadd(InstSet,'Swap', LegRate, Settle, '27 Feb 2010'); % Puttable and Callable Bonds InstSet = instadd(InstSet,'OptEmBond', CouponRate, Settle, '27 Feb 2010', {'put';'call'},... Strike, '27 Feb 2010','AmericanOpt', 1, 'Period', 1); % Process Names and Holdings Holdings = [15 ;5 ;8; 7; 9; 4]; Names = {'3.5% Cap';'5% Floor';'30BP Float';'4%/5BP Swap'; 'PuttBond'; 'CallBond' }; InstSet = instsetfield(InstSet, 'Index',4:9, 'FieldName', {'Quantity'}, 'Data', Holdings ); InstSet = instsetfield(InstSet, 'Index',4:9, 'FieldName', {'Name'}, 'Data', Names );

Examine the set of instruments contained in the variable `InstSet`

.

instdisp(InstSet)

Index Type CouponRate Settle Maturity Period Basis EndMonthRule IssueDate FirstCouponDate LastCouponDate StartDate Face Quantity Name 1 Bond 0.04 27-Feb-2007 27-Feb-2009 1 0 1 NaN NaN NaN NaN 100 10 4% Bond 2 Bond 0.05 27-Feb-2007 27-Feb-2010 1 0 1 NaN NaN NaN NaN 100 15 5% Bond Index Type UnderInd OptSpec Strike ExerciseDates AmericanOpt Quantity Name 3 OptBond 2 call 98 27-Feb-2010 1 3 Option 98 Index Type Strike Settle Maturity CapReset Basis Principal Quantity Name 4 Cap 0.035 27-Feb-2007 27-Feb-2010 1 0 100 15 3.5% Cap Index Type Strike Settle Maturity FloorReset Basis Principal Quantity Name 5 Floor 0.05 27-Feb-2007 27-Feb-2009 1 0 100 5 5% Floor Index Type Spread Settle Maturity FloatReset Basis Principal EndMonthRule CapRate FloorRate Quantity Name 6 Float 30 27-Feb-2007 27-Feb-2009 1 0 100 1 Inf -Inf 8 30BP Float Index Type LegRate Settle Maturity LegReset Basis Principal LegType EndMonthRule StartDate Quantity Name 7 Swap [0.04 5] 27-Feb-2007 27-Feb-2010 [NaN] 0 100 [NaN] 1 NaN 7 4%/5BP Swap Index Type CouponRate Settle Maturity OptSpec Strike ExerciseDates Period Basis EndMonthRule IssueDate FirstCouponDate LastCouponDate StartDate Face AmericanOpt Quantity Name 8 OptEmBond 0.04 27-Feb-2007 27-Feb-2010 put 98 27-Feb-2007 27-Feb-2010 1 0 1 NaN NaN NaN NaN 100 1 9 PuttBond 9 OptEmBond 0.05 27-Feb-2007 27-Feb-2010 call 98 27-Feb-2007 27-Feb-2010 1 0 1 NaN NaN NaN NaN 100 1 4 CallBond

**Hedging**

The idea behind hedging is to minimize exposure to market movements. As the underlying changes, the proportions of the instruments forming the portfolio may need to be adjusted to keep the sensitivities within the desired range.

Calculate sensitivities using the BK model.

[Delta, Gamma, Vega, Price] = bksens(BKTree, InstSet);

Get the current portfolio holdings.

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

Create 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.

```
format bank
disp([Price Holdings Sensitivities])
```

98.88 10.00 -185.47 528.47 0 101.35 15.00 -277.51 1045.05 0 3.35 3.00 -223.52 11843.32 0 2.77 15.00 250.04 2921.11 -0.00 0.75 5.00 -132.97 11566.69 0 100.56 8.00 -0.80 2.02 0 -1.53 7.00 -272.08 1027.85 0.00 98.60 9.00 -168.92 21712.82 0 98.00 4.00 -53.99 -10798.27 0

The first column above is the dollar unit price of each instrument, the second column is the number of contracts of each instrument, and the third, fourth, and fifth columns are the dollar delta, gamma, and vega sensitivities.

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

TargetSens = Holdings' * Sensitivities

`TargetSens = `*1×3*
-7249.21 317573.92 -0.00

**Obtain a Neutral Sensitivity Portfolio Using hedgeslf**

Suppose you want to obtain a delta, gamma and vega neutral portfolio. The function `hedgeslf`

finds the reallocation in a portfolio of financial instruments closest to being self-financing (maintaining constant portfolio value).

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

`Sens = `*3×1*
0.00
-0.00
-0.00

Value1 = 4637.54

`Quantity = `*9×1*
10.00
5.26
-5.11
7.06
-3.05
12.45
-7.36
8.47
10.37

The function `hedgeslf`

returns the portfolio dollar sensitivities (`Sens`

), the value of the rebalanced portfolio (`Value1`

) and the new allocation for each instrument (`Quantity`

). If `Value0`

and `Value1`

represent the portfolio value before and after rebalancing, you can verify the cost by comparing the portfolio values.

Value0 = Holdings' * Price

Value0 = 4637.54

In this example, the portfolio is fully hedged (simultaneous delta, gamma, and vega neutrality) and self-financing (the values of the portfolio before and after balancing (`Value0`

and `Value1`

) are the same.

**Adding Constraints to Hedge a Portfolio**

Suppose that you want to place upper and lower bounds on the individual instruments in the portfolio. Let's say that you want to bound the position of all instruments to within +/- 11 contracts.

Applying these constraints disallows the current positions in the fifth and eighth instruments. All other instruments are currently within the upper/lower bounds.

% Specify the lower and upper bounds LowerBounds = [-11 -11 -11 -11 -11 -11 -11 -11 -11]; UpperBounds = [ 11 11 11 11 11 11 11 11 11]; % Use the function portcons to build the constraints ConSet = portcons('AssetLims', LowerBounds, UpperBounds); % Apply the constraints to the portfolio [Sens, Value, Quantity1] = hedgeslf(Sensitivities, Price, Holdings, [], ConSet)

`Sens = `*3×1*
0
0
0

Value = 0

`Quantity1 = `*9×1*
0
0
0
0
0
0
0
0
0

Observe that the `hedgeslf`

function enforces the bounds on the fifth and eighth instruments, and the portfolio continues to be fully hedged and self-financing.

`set(0, 'format', OldFormat);`

## 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

`1`

(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]
```

. The default is `FixedInd`

=
`1`

; the holdings in the first instrument are held
fixed. If no instruments are to be held fixed, enter
`FixedInd`

= `[ ]`

.

**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**

Constraints ```
PortHolds(FixedInd) =
CurrentHolds(FixedInd)
```

are appended to any constraints
passed in `ConSet`

. Pass
`FixedInd`

= `[ ]`

to
specify all constraints through `ConSet`

. The
default constraints generated by `portcons`

are
inappropriate, since they require the sum of all holdings to be
positive and equal to `1`

.

**Data Types: **`double`

## Output Arguments

`PortSens`

— Portfolio dollar sensitivities

vector

Portfolio dollar sensitivities, returned as a number of points
`NSENS`

-by-`1`

vector. When a perfect
hedge exists, `PortSens`

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

`PortValue`

— Total portfolio value

numeric

Total portfolio value, returned as a scalar value. When a perfectly
self-financing hedge exists, `PortValue`

is equal to the
value `dot(Price,CurrentHolds)`

of the initial
portfolio.

`PortHolds`

— Contracts allocated to each instrument

vector

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

-by-`1`

vector. This is the
reallocated portfolio.

## Version History

**Introduced before R2006a**

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