//+------------------------------------------------------------------+
//|                                                     GJR_Demo.mq5 |
//|                                  Copyright 2025, MetaQuotes Ltd. |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2025, MetaQuotes Ltd."
#property link      "https://www.mql5.com"
#property version   "1.00"
#property script_show_inputs
#include<VolatilityModels\Arch\Univariate\mean.mqh>
//--- input parameters
input string   Symbol_="AUDUSD";
input ENUM_TIMEFRAMES   TimeFrame=PERIOD_D1;
input datetime StartDate=D'2025.01.01';
input ulong HistoryLen = 504;
input double ScaleFactor=100.;
input bool MeanConstant = true;
input ulong _P_ = 1;
input ulong _O_ = 1;
input ulong _Q_ = 1;
//+------------------------------------------------------------------+
//| Script program start function                                    |
//+------------------------------------------------------------------+
void OnStart()
  {
   if(_O_<1)
     {
      Alert(" invalid setting, GJR GARCH models need an asymmetry parameter of at least 1\n Otherwise it becomes a regular GARCH model");
      return;
     }
//---download data
   vector prices;
   if(!prices.CopyRates(Symbol_,TimeFrame,COPY_RATES_CLOSE,StartDate,HistoryLen))
      {
       Print(" failed to get close prices for ", Symbol_,". Error ", GetLastError());
       return;
      }
//---
   prices = log(prices);
//---
   vector returns = np::diff(prices);
//-- specify the model parameters
   ArchParameters gjr_spec;
   gjr_spec.observations=ScaleFactor*returns;
   gjr_spec.include_constant = MeanConstant;
   gjr_spec.vol_model_type = VOL_GJR_GARCH;
   gjr_spec.garch_p = _P_;
   gjr_spec.garch_q = _Q_;
   gjr_spec.garch_o = _O_;
//-- initialize the mean model
   ConstantMean gjr_model;
//---
   if(!gjr_model.initialize(gjr_spec))
      return;
//---get the model parameters
   vector empty;
   ArchModelResult gjr_params = gjr_model.fit(ScaleFactor);
//--check the model is fitted
   if(!gjr_params.params.Size())
     {
      Print("Convergence failed ", GetLastError());
      return;
     }
//---
   Print("GJR-GARCH model parameters ", gjr_params.params);
//---
   Print("GJR-GARCH model parameters ", gjr_params.params);
   vector pv = gjr_params.pvalues();
   Print("GJR-GARCH model pvalues: ");
   for(ulong i = 0; i<pv.Size(); ++i)
    Print(" pvalue for param at ", i , " :- ", pv[i]);
//---
  }
//+------------------------------------------------------------------+