98 lines
3.8 KiB
MQL5
98 lines
3.8 KiB
MQL5
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//+------------------------------------------------------------------+
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//| Impact_Curves.mq5 |
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//| Copyright 2025, MetaQuotes Ltd. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2025, MetaQuotes Ltd."
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#property link "https://www.mql5.com"
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#property version "1.00"
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#resource "\\Files\\VolatilityModels\\sp500.csv" as string equity_data
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#property script_show_inputs
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#include<VolatilityModels\Arch\Univariate\mean.mqh>
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//--- input parameters
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input double ScaleFactor=100.;
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input ulong _P_ = 1;
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input ulong _O_ = 1;
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input ulong _Q_ = 1;
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//+------------------------------------------------------------------+
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//| Script program start function |
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//+------------------------------------------------------------------+
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void OnStart()
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{
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//---
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matrix data = np::readcsv_from_string(equity_data,false,",",true,0);
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vector returns = log(data.Col(1));
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returns = np::diff(returns);
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//---
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returns*=ScaleFactor;
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//---
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ArchParameters gjr_spec,tarch_spec,garch_spec;
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//---
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gjr_spec.mean_model_type = MEAN_CONSTANT;
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gjr_spec.observations = returns;
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gjr_spec.garch_p = _P_;
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gjr_spec.garch_q = _Q_;
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gjr_spec.garch_o = _O_;
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//---
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garch_spec = gjr_spec;
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tarch_spec = gjr_spec;
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//---
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gjr_spec.vol_model_type = VOL_GJR_GARCH;
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garch_spec.vol_model_type = VOL_GARCH;
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tarch_spec.vol_model_type = VOL_TARCH;
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//---
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ConstantMean gjr_model;
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ConstantMean garch_model;
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ConstantMean tarch_model;
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//---
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if(!gjr_model.initialize(gjr_spec) || !garch_model.initialize(garch_spec) || !tarch_model.initialize(tarch_spec))
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return;
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//---
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ArchModelResult gjr_params = gjr_model.fit(ScaleFactor);
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//---
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ArchModelResult tarch_params = tarch_model.fit(ScaleFactor);
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//---
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ArchModelResult garch_params = garch_model.fit(ScaleFactor);
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//---
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vector shocks = np::linspace(-5.0,5.0,100);
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//---
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vector gjr_response,tarch_response,garch_response;
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//---
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gjr_response = gjr_impact(shocks,gjr_params.params);
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tarch_response = tarch_impact(shocks,tarch_params.params);
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garch_response = garch_impact(shocks,garch_params.params);
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matrix xaxis,yaxis;
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xaxis = np::vectorAsRowMatrix(shocks,3);
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yaxis = matrix::Zeros(3,shocks.Size());
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yaxis.Row(gjr_response,0);
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yaxis.Row(tarch_response,1);
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yaxis.Row(garch_response,2);
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//---
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np::plotmatrices(xaxis,yaxis," Impact curves",false,"Shocks(%)","Predicted Volatility","GJR-GARCH(volatility),TARCH(volatility),GARCH(volatility)",CURVE_LINES,3,0,0,0,0,750,400,true,40);
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}
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//+------------------------------------------------------------------+
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//+------------------------------------------------------------------+
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//| Function to calculate GJR-GARCH response |
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//+------------------------------------------------------------------+
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vector gjr_impact(vector& z, vector& p)
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{
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vector isless_than_zero = np::whereVectorIsLt(z,0.0);
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return p[1] + p[2] * pow(z,2.) + p[3] * (isless_than_zero) * pow(z,2.0);
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}
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//+------------------------------------------------------------------+
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//| Function to calculate GARCH response |
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//+------------------------------------------------------------------+
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vector garch_impact(vector& z, vector& p)
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{
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return p[1] + p[2] * pow(z,2.) + p[3] * pow(z,2.0);
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}
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//+------------------------------------------------------------------+
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//| Function to calculate TARCH response |
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//+------------------------------------------------------------------+
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vector tarch_impact(vector& z, vector& p)
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{
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vector isless_than_zero = np::whereVectorIsLt(z,0.0);
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return p[1] + p[2] * fabs(z) + p[3] * (isless_than_zero) * fabs(z);
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}
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