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Targets.gms
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$TITLE Equal Weight Portfolio
* Generates targets based on 1/N portfolio
$eolcom //
option optcr=0.0001;
SETS
Date 'Dates'
Asset 'Assets'
Periods 'Amount of 4-week intervals'
Scenarios 'Scenarios'
;
ALIAS(Date,t);
ALIAS(Asset,i);
ALIAS(Periods,p);
ALIAS(Scenarios,s);
$onMulti
PARAMETER
ScenarioReturnP(i,s,p) 'Return of index i, at date t, in scenario interval p'
ReturnsMonth(p,i) 'Return of index i in the ord(p)th month'
;
*Loading asset, periods, scenarios and scenario returns data from "RollingScenarios.gdx"
$GDXIN RollingScenarios
$LOAD Asset Periods Scenarios ScenarioReturnP
$GDXIN
DISPLAY Asset, Periods, Scenarios, ScenarioReturnP;
*Loading the actual monthly returns
$GDXIN ReturnMonthly
$LOAD ReturnsMonth
$GDXIN
SCALARS
Budget 'Nominal investment budget'
alpha 'Confidence level'
Lambda 'Risk aversion parameter'
CVaRLim 'CVaR limit'
ExpRetLim 'Expected Return Limit'
;
// DEFINE BUDGET AND ALPHA
Budget = 100000.0;
alpha = 0.95;
PARAMETERS
pr(s) 'Scenario probability'
Rp(i,s,p) 'Final values "R_is" for ord(p)th rolling'
EPp(i,p) 'Expected final values "mu_i" for ord(p)th rolling'
R(i,s) 'Auxiliary parameter for final values'
EP(i) 'Auxiliary parameter for Expected final values'
;
// DEFINITIONS
pr(s) = 1.0 / CARD(s);
Rp(i,s,p) = 1 + ScenarioReturnP(i,s,p);
EPp(i,p) = SUM(s, pr(s) * Rp(i,s,p));
PARAMETER x_old(i);
POSITIVE VARIABLES
x(i) 'Holdings of assets in proportions'
VaRDev(s) 'Measures of the deviation'
;
VARIABLES
Losses(s) 'The scenario loss function'
VaR 'The alpha Value-at-Risk'
CVaR 'The alpha Conditional Value-at-Risk'
ExpectedReturn 'Expected return of the portfolio'
obj 'Objective function value'
;
EQUATIONS
BudgetCon 'Equation defining the budget constraint'
ReturnCon 'Equation defining the portfolio expected return'
LossDefCon(s) 'Equation defining the losses'
VaRDevCon(s) 'Equation defining the VaRDev variable'
CVaRDefCon 'Equation defining the CVaR'
ObjectivFunc 'Lambda formulation of the MeanCVaR model'
;
*--s.t.-----------
BudgetCon .. sum(i, x(i)) =E= Budget;
ReturnCon .. ExpectedReturn =E= sum(i, EP(i)*x(i));
LossDefCon(s) .. Losses(s) =E= -1*sum(i, R(i, s)*x(i) );
VaRDevCon(s) .. VaRDev(s) =G= Losses(s) - VaR;
CVaRDefCon .. CVaR =E= VaR + (sum(s, pr(s)*VarDev(s) ) )/(1 - alpha);
*--Objective------
ObjectivFunc .. Obj =E= (1-lambda)*ExpectedReturn - lambda*CVaR;
*--Model-----------
MODEL CVaRModel 'The Conditional Value-at-Risk Model' /BudgetCon, ReturnCon, LossDefCon, VaRDevCon, CVaRDefCon/;
*------------CVaR----------------------
PARAMETERS
ExpectedReturnp(p) 'Expected return for ord(p)th rolling'
VaRp(p) 'Value-at-risk for ord(p)th rolling'
CVaRp(p) 'Conditional-Value-at-risk for ord(p)th rolling'
RunningBudget(p) '1/N portfolio performance'
;
// THE FIRST PERIOD
X.fx(i) = Budget/card(i);
display X.l;
R(i,s) = Rp(i,s,"p0");
EP(i) = SUM(s, pr(s) * R(i,s));
SOLVE CVaRModel Minimazing CVaR using LP;
ExpectedReturnp("p0") = (ExpectedReturn.l/Budget) - 1;
CVaRp("p0") = CVaR.l/Budget;
RunningBudget("p0")=Budget;
display x.l
// REBALANCING
LOOP(p$(ord(p)>=2),
R(i,s) = Rp(i,s,p);
EP(i) = SUM(s, pr(s) * R(i,s));
x_old(i) = x.l(i)*(1 + ReturnsMonth(p,i));
Budget = sum(i,x_old(i));
RunningBudget(p) = Budget;
x.fx(i) = x_old(i);
SOLVE CVaRModel MINIMIZING CVaR using LP;
CVaRModel.SOLPRINT = 2;
ExpectedReturnp(p) = (ExpectedReturn.l/sum(i,x.l(i)))-1;
CVaRp(p) = CVaR.l/sum(i,x.l(i));
);
parameter SummaryReport(*,*);
* Store results by rows
SummaryReport('ExpectedReturn',p) = ExpectedReturnp(p);
SummaryReport('CVaR',p) = CVaRp(p);
SummaryReport('EqualWeightPortfolio',p)= RunningBudget(p);
display SummaryReport
EXECUTE_UNLOAD 'TargetsRolling.gdx' SummaryReport;
*EXECUTE 'gdxxrw.exe Targets.gdx O=Targets.xls par=SummaryReport rng=sheet1!a1';