Artigos Acadêmicos e Noticiosos

URI permanente desta comunidadehttps://repositorio.insper.edu.br/handle/11224/3226

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Data inicial: 2024Palavra-chave: search.filters.subject.Sparsity

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    Artigo Científico
    Stochastic Volatility Models with Skewness Selection
    (2024) Martins, Igor; HEDIBERT FREITAS LOPES
    This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.
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    Artigo Científico
    Sparse Bayesian Factor Analysis When the Number of Factors Is Unknown
    (2024) Frühwirth-Schnatter, Sylvia; Hosszejni, Darjus; HEDIBERT FREITAS LOPES
    There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonity of factor models. In this spirit, we estimate the number of common factors in the widely applied sparse latent factor model with spike-and-slab priors on the factor loadings matrix. Our framework leads to a natural, efficient and simultaneous coupling of model estimation and selection on one hand and model identification and rank estimation (number of factors) on the other hand. More precisely, by embedding the unordered generalised lower trian gular loadings representation into overfitting sparse factor modelling, we obtain posterior summaries regarding factor loadings, common factors as well as the factor dimension via postprocessing draws from our efficient and customized Markov chain Monte Carlo scheme.