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Item type:Publication, Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models(Elsevier, 2020-04-01)Using weekly data for stock and Forex market returns, a set of MS-GARCH models is estimated for a group of high-income (HI) countries and emerging market economies (EMEs) using algorithms proposed by Augustyniak (2014) and Ardia et al. (2018, 2019a,b), allowing for a variety of conditional variance and distribution specifications. The main results are: (i) the models selected using Ardia et al. (2018) have a better fit than those estimated by Augustyniak (2014), contain skewed distributions, and often require that the main coefficients be different in each regime; (ii) in Latam Forex markets, estimates of the heavy-tail parameter are smaller than in HI Forex and all stock markets; (iii) the persistence of the high-volatility regime is considerable and more evident in stock markets (especially in Latam EMEs); (iv) in (HI and Latam) stock markets, a single-regime GJR model (leverage effects) with skewed distributions is selected; but when using MS models, virtually no MS-GJR models are selected. However, this does not happen in Forex markets, where leverage effects are not found either in single-regime or MS-GARCH models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The impact of research on health education/health literacy on policymaking in Latin America and the Caribbean Region(American Institute of Mathematical Sciences, 2024-01-01)In this study, we addressed the gap between health research and policymaking in Latin America and the Caribbean (LAC), focusing on health education/health literacy. Despite growing research, translating findings into effective policies needs to be improved. We explored the factors that make research on health education and health literacy to be referenced and mentioned in policy documents in LAC (and in Peru). We proposed a model based on the hypothesis that the relationship between research and policymaking depends on the research strength of scientific evidence, timing, and social media activity. Methods: A mixed-methods approach was employed, combining quantitative and qualitative data analysis. Quantitative data sources included multidisciplinary databases, altmetric data, and citations of policy documents. For data analysis, we obtained descriptive statistics to identify patterns and then verified the association between variables using χ2. The negative binomial regression was used to test the empirical model introduced above. Quantitative analysis was complemented by analysis of responses to a set of open questions from a sample of Peruvian health policymakers. Results: We found that timing, strength of evidence, and social media activity were significant predictors of research cited in policy documents. Policy documents tended to rely more on qualitative evidence. A positive correlation between timing and cites in policy documents highlighted the importance of timely dissemination, whereas social media activity, while having an impact, had a relatively minor effect. Peruvian policymakers' responses emphasized the role of political context, the relevance of results, and policymakers' commitment to incorporating research into policies. Conclusion: Strength of evidence, social media engagement, and publication timing are key predictors of citations for health education/literacy research in LAC policy documents. However, qualitative findings highlight challenges, including some distrust in research findings, together with limited access to relevant research. The findings offer opportunities to enhance evidence-informed health education/health literacy policy decisions. Implications: To increase the influence on health policymakers, researchers should prioritize the timely dissemination of solid evidence, considering both traditional and digital platforms. Policymakers should focus on the quality and relevance of evidence when formulating policies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting value at risk and expected shortfall in equity markets of high-income and Latin American countries(Pontificia Universidad Católica del Perú. Departamento de Economía, 2026-02)Using daily equity market data for Latin American (Latam) and high-income (HI) countries over 2008-2023, this paper estimates GARCH and GJR models to forecast Value at Risk (VaR) and Expected Shortfall (ES). The performance of a broad set of heavy-tailed and asymmetric distributions is evaluated, including the Normal (N), Skewed Normal (skN), Student’s t (S), skewed S (skS), generalized hyperbolic skS (GHskS), normal inverse Gaussian (NIG), skewed NIG (skNIG), normal reciprocal inverse Gaussian (NRIG), and skewed NRIG (skNRIG). The key findings can be summarized as follows: (i) for VaR forecasting, asymmetric distributionsare preferred at both confidence levels, and at the 99% level heavy tails are also required; (ii) for ES forecasting, at both confidence levels the selected models rely on asymmetric heavy-tailed distributions, with GHskS emerging as the dominant specification; (iii) for VaR forecasting, modeling leverage effects is necessary for most HI countries, whereas this is required for only about half of the Latam countries; and (iv) for ES forecasting, volatility specification plays a more limited role than in VaR forecasting.