Examinando por Autor "Nave Pineda, Juan M."

Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Effects of uncertainty and risk aversion on the exposure of investment-style factor returns to real activity
    (Elsevier, 2020) Nave Pineda, Juan M.; Rubio Irigoyen, Gonzalo; González Sánchez, Mariano
    How do uncertainty and risk aversion affect the behavior of investment-style factors? We argue that a significant channel through which both uncertainty and risk aversion impact aggregate risk factors is the exposure of factor returns to real activity. We analyze this issue using mixed data sampling decomposition of the sensitivity of factor returns to real activity into high- and lowfrequency components. We find a positive and significant relation between uncertainty and risk aversion for the low-frequency component of the sensitivity of factor returns to economic activity. More importantly, risk aversion significantly amplifies the effects of uncertainty on real activity exposure. The quality-based factor is an important exception to these findings.
  • Miniatura
    Publicación
    La estimación de la estructura temporal de los tipos de interés : metodología y aplicaciones
    (Universidad Nacional de Educación a Distancia (España). Facultad de Ciencias Económicas y Empresariales. Departamento de Economía y Contabilidad, 2009-07-14) Berenguer Cárceles, Emma; Nave Pineda, Juan M.; Jimeno Nogués, Ricardo
  • Miniatura
    Publicación
    Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement
    (Elsevier, 2023) Nave Pineda, Juan M.; González Sánchez, Mariano
    Estimating the market risk is conditioned by the fat tail of the distribution of returns. But the tail index depends on the threshold of this distribution fat tail. We propose a methodology based on the decomposition of the series into positive outliers, Gaussian central part and negative outliers and uses the latter to estimate this cutoff point. Additionally, from this decomposition, we estimate extreme dependence correlation matrix which is used in the measurement of portfolio risk. For a sample consisting of six assets (Bitcoin, Gold, Brent, Standard&Poor-500, Nasdaq and Real Estate index), we find that our methodology presents better results, in terms of normality and volatility of the tail index, than the Kolmogorov–Smirnov distance, and its unnecessary capital consumption is lower. Also, in the measurement of the risk of a portfolio, the results of our proposal improve those of a t-Student copula and allow us to estimate the extreme dependence and the corresponding indexes avoiding the implicit restrictions of the elliptic and Archimedean copulas.