InternationalMeanVar : International Asset Allocation model.

**Description**

InternationalMeanVar.gms: International asset allocation model. Consiglio, Nielsen and Zenios. PRACTICAL FINANCIAL OPTIMIZATION: A Library of GAMS Models, Section 3.5 Last modified: Apr 2008.

**Category :** GAMS FIN library

**Mainfile :** InternationalMeanVar.gms **includes :** Estimate.gdx

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$TITLE International asset allocation model
* InternationalMeanVar.gms: International asset allocation model.
* Consiglio, Nielsen and Zenios.
* PRACTICAL FINANCIAL OPTIMIZATION: A Library of GAMS Models, Section 3.5
* Last modified: Apr 2008.
* We use real data for the 10-year period 1990-01-01 to 2000-01-01,
*
* 23 Italian Stock indices
* 3 Italian Bond indices (1-3yr, 3-7yr, 5-7yr)
* Italian risk-free rate (3-month cash)
*
* 7 international Govt. bond indices
* 5 Regions Stock Indices: (EMU, Eur-ex-emu, PACIF, EMER, NORAM)
* 3 risk-free rates (3-mth cash) for EUR, US, JP
*
* US Corporate Bond Sector Indices (Finance, Energy, Life Ins.)
*
* Exchange rates, ITL to: (FRF, DEM, ESP, GBP, US, YEN, EUR)
* Also US to EUR.
SET Assets;
ALIAS(Assets,i,j);
SET
IT_STOCK(i) Italian stock indexes
IT_ALL(i) Italian stock plus bond indexes
INT_STOCK(i) Italian and international stock indexes
INT_ALL(i) Italian stock and government indexes, international stock and government indexes, plus corporate indexes
PARAMETERS
MAX_MU Maximum level of the expected return
ExpectedReturns(i) Expected returns
VarCov(i,j) Variance-Covariance matrix
RiskFree Risk free return;
$gdxin Estimate
$LOAD Assets IT_STOCK IT_ALL INT_STOCK INT_ALL
$LOAD MAX_MU ExpectedReturns=MU VarCov=Q RiskFree=RiskFreeRt
$gdxin
SET ACTIVE(ASSETS);
alias(ACTIVE, a, a1, a2);
* Target return
SCALAR
MU_TARGET Target portfolio return
MU_STEP Target return step;
* Assume we want 20 portfolios in the frontier
MU_STEP = MAX_MU / 20;
POSITIVE VARIABLES
x(i) Holdings of assets;
VARIABLES
PortVariance Portfolio variance;
EQUATIONS
ReturnCon Equation defining the portfolio return constraint
VarDef Equation defining the portfolio variance
NormalCon Equation defining the normalization contraint;
ReturnCon .. SUM(a, ExpectedReturns(a)*x(a)) =E= MU_TARGET;
VarDef .. PortVariance =E= SUM((a1,a2), x(a1)*VarCov(a1,a2)*x(a2));
NormalCon .. SUM(a, x(a)) =E= 1;
OPTION SOLVEOPT = REPLACE;
MODEL MeanVar /ReturnCon,VarDef,NormalCon/;
FILE FrontierHandle /"InternationalMeanVarFrontier.csv"/;
FrontierHandle.pc = 5;
FrontierHandle.pw = 1048;
PUT FrontierHandle;
* Step 1: First solve only for Italian stocks:
ACTIVE(i) = IT_STOCK(i);
PUT "Step 1: Italian stock assets"/;
PUT "Variance","ExpReturn";
* Asset labels
LOOP (i, PUT i.tl);
PUT /;
FOR (MU_TARGET = 0 TO MAX_MU BY MU_STEP,
SOLVE MeanVar MINIMIZING PortVariance USING nlp;
PUT PortVariance.l:6:5, MU_TARGET:6:5;
LOOP (i, PUT x.l(i):6:5 );
PUT /;
);
*
* Step 2: Now solve for Italian stock and government indices:
*
PUT "Step 2: Italian stock and government assets"/;
ACTIVE(i) = IT_ALL(i);
FOR (MU_TARGET = 0 TO MAX_MU BY MU_STEP,
SOLVE MeanVar MINIMIZING PortVariance USING nlp;
PUT PortVariance.l:6:5, MU_TARGET:6:5;
LOOP (i, PUT x.l(i):6:5 );
PUT /;
);
*
* Step 3: Italian stock plus international stock indices
*
PUT "Step 3: Italian and international stock indices"/;
ACTIVE(i) = INT_STOCK(i);
FOR (MU_TARGET = 0 TO MAX_MU BY MU_STEP,
SOLVE MeanVar MINIMIZING PortVariance USING nlp;
PUT PortVariance.l:6:5, MU_TARGET:6:5;
LOOP (i, PUT x.l(i):6:5 );
PUT /;
);
*
* Step 4: Italian stock and government indices, international stock and government
* indices, plus corporate indices.
*
PUT "Step 4: All indices"/;
ACTIVE(i) = INT_ALL(i);
FOR (MU_TARGET = 0 TO MAX_MU BY MU_STEP,
SOLVE MeanVar MINIMIZING PortVariance USING nlp;
PUT PortVariance.l:6:5, MU_TARGET:6:5;
LOOP (i, PUT x.l(i):6:5 );
PUT /;
);
*
* Step 5: All italian stock indices plus risk free
*
VARIABLES
z
d_bar;
EQUATIONS
RiskFreeReturnDef
SharpeRatio;
RiskFreeReturnDef .. d_bar =E= SUM(a, ExpectedReturns(a)*x(a)) - RiskFree;
SharpeRatio .. z =E= d_bar / sqrt( PortVariance );
MODEL Sharpe /RiskFreeReturnDef,VarDef,NormalCon,SharpeRatio/;
SOLVE Sharpe MAXIMIZING z USING nlp;
* Write the variance and expected return for the tangent portfolio
PUT "Step 5: Tangent portfolio"/;
PUT PortVariance.l:6:5, (d_bar.l + RiskFree):6:5, z.l:6:5;
* Write the tangent portfolio.
LOOP (i, PUT x.l(i):6:5 );
PUT /;
*
* Step 6: Include the total Italian stock index as a liability
*
*
* Build a model (very similar to the previous one)
* which attempts to track (synthesize) the Italian total stock index,
* ITMHIST, using the 23 Italian stock indices and 3 Italian bond indices
* plus the Italian risk-free asset.
*
* This is done by including ITMHIST as an asset but fixing its weight
* in the portfolio at -1. The 26 other assets then must try to balance
* out the variance of ITMHIST. In addition, we pursue different levels
* of expected return (over and above the ITMHIST return).
* Create a convenient subset containing only the general Italian stock index:
SET It_general(ASSETS) / ITMHIST /;
* The only constraint which need to be redefined is the
* normalization constraint. Indeed, it must be se to 0.
EQUATIONS
NormalConTrack Equation defining the normalization contraint for tracking;
NormalConTrack .. SUM(a, x(a)) =E= 0;
OPTION SOLVEOPT = REPLACE;
MODEL MeanVarTrack /ReturnCon,VarDef,NormalConTrack/;
x.FX(It_general) = -1;
PUT "Step 6: Index tracking"/;
ACTIVE(i) = IT_STOCK(i) or It_general(i);
* Re-estimate MU_STEP as MAX_MU is different for the tracking problem
MAX_MU = 0.1587;
MU_STEP = MAX_MU / 20;
FOR (MU_TARGET = 0 TO MAX_MU BY MU_STEP,
SOLVE MeanVarTrack MINIMIZING PortVariance USING nlp;
PUT MeanVarTrack.MODELSTAT:0:0, PortVariance.l:6:5, MU_TARGET:6:5;
LOOP (i, PUT x.l(i):6:5 );
PUT /;
);
PUTCLOSE;
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