Accurate Value at Risk measurement often requires estimation of complex dynamic models where usually the parameters enter nonlinearly the quantile estimation equation. IN this paper we address the problem of estimation of the parameters of a class of conditionally autoregressive Value at Risk models by adapting the Majorizing-Minorizing algorithm of Hunter and Lange (2000)
Estimation of dynamic quantile models via the MM algorithm / Poggioni, Fabrizio; Bernardi, Mauro; Petrella, Lea. - (2019), pp. 1033-1038.
Estimation of dynamic quantile models via the MM algorithm
Fabrizio Poggioni;Lea Petrella
2019
Abstract
Accurate Value at Risk measurement often requires estimation of complex dynamic models where usually the parameters enter nonlinearly the quantile estimation equation. IN this paper we address the problem of estimation of the parameters of a class of conditionally autoregressive Value at Risk models by adapting the Majorizing-Minorizing algorithm of Hunter and Lange (2000)File allegati a questo prodotto
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