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 = datetime(2007,2,27); Rates = [0.0493; 0.0459; 0.0450; 0.0446; 0.0446; 0.0450; 0.0473]; EndDates = [datetime(2008,2,27); datetime(2009,2,27) ; datetime(2010,2,27) ; datetime(2012,2,27) ; datetime(2014,2,27) ; datetime(2017,2,27) ; datetime(2027,2,27)]; 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: {[7x1 double] [6x1 double] [5x1 double] [4x1 double] [3x1 double] [2x1 double] [20]}
Probs: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double]}
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 4 5 6 7 8 8]}
FwdTree: {[1.0493] [1.0434 1.0425 1.0416] [1.0457 1.0444 1.0432 1.0420 1.0409] [1.1003 1.0967 1.0933 1.0899 1.0867 1.0836 1.0806] [1.1073 1.1016 1.0962 1.0911 1.0863 1.0818 1.0775] [1x9 double] [1x9 double]}
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: {[7x1 double] [6x1 double] [5x1 double] [4x1 double] [3x1 double] [2x1 double] [20]}
Probs: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double]}
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 4 5 6 7 8 8]}
RateTree: {[0.0481] [0.0425 0.0416 0.0408] [0.0446 0.0434 0.0423 0.0412 0.0401] [0.0478 0.0462 0.0446 0.0430 0.0416 0.0401 0.0388] [0.0510 0.0484 0.0459 0.0436 0.0414 0.0393 0.0373] [1x9 double] [1x9 double]}
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 = datetime(2007,2,27); Maturity = [datetime(2009,2,27) ; datetime(2010,2,27) ]; Period = 1; % American Option on the 5% Bond OptSpec = {'call'}; Strike = 98; ExerciseDates = datetime(2010,2,27); 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: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double] [3x9 double] [3x9 double]}
AITree: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double] [3x9 double] [3x9 double]}
ExTree: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double] [3x9 double] [3x9 double]}
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 4 5 6 7 8 8]}
Probs: {[3x1 double] [3x3 double] [3x5 double] [3x7 double] [3x7 double] [3x9 double]}
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, datetime(2010,2,27)); % Floor StrikeF =0.05; InstSet = instadd(InstSet,'Floor', StrikeF, Settle, datetime(2009,2,27)); % Floating Rate Note InstSet = instadd(InstSet,'Float', 30, Settle, datetime(2009,2,27)); % Vanilla Swap LegRate =[0.04 5]; InstSet = instadd(InstSet,'Swap', LegRate, Settle, datetime(2010,2,27)); % Puttable and Callable Bonds InstSet = instadd(InstSet,'OptEmBond', CouponRate, Settle,datetime(2010,2,27), {'put';'call'},... Strike, datetime(2010,2,27),'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);See Also
Related Examples
- Portfolio Creation Using Functions
- Adding Instruments to an Existing Portfolio Using Functions
- Instrument Constructors
- Creating Instruments or Properties
- Searching or Subsetting a Portfolio
- Pricing a Portfolio Using the Black-Derman-Toy Model
- Specifying Constraints with ConSet
- Portfolio Rebalancing
- Hedging with Constrained Portfolios
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