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  "Title": "PSO Based Weighted Ensemble Algorithm for Volatility Modelling",
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  "Authors@R": "c(person(\"Mr. Ankit Kumar\", \"Singh\", role = \"aut\", email = \"ankitsinghvns32@gmail.com\"),\nperson(\"Dr. Ranjit Kumar\", \"Paul\", role = c(\"aut\",\"cre\"), email = \"ranjitstat@gmail.com\"),\nperson(\"Dr. Amrit Kumar\", \"Paul\", role = \"aut\"),\nperson(\"Dr. Md\", \"Yeasin\", role = \"aut\"),\nperson(\"Ms. Anita\", \"Sarkar\", role = \"aut\")\n)",
  "Author": "Mr. Ankit Kumar Singh [aut], Dr. Ranjit Kumar Paul [aut, cre],\nDr. Amrit Kumar Paul [aut], Dr. Md Yeasin [aut], Ms. Anita\nSarkar [aut]",
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  "Description": "Price volatility refers to the degree of variation in\nseries over a certain period of time. This volatility is\nespecially noticeable in agricultural commodities, adding\nuncertainty for farmers, traders, and others in the\nagricultural supply chain. Commonly and popularly used four\nvolatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH\n(GJR-GARCH) model, exponentially weighted moving average (EWMA)\nmodel and Multiplicative Error Model (MEM) are selected and\nimplemented. PWAVE, weighted ensemble model based on particle\nswarm optimization (PSO) is proposed to combine the forecast\nobtained from all the candidate models. This package has been\ndeveloped using algorithm of Paul et al.\n<doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024)\n<doi:10.1007/s11227-023-05542-3>.",
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