Examinando por Autor "Leal, Danilo"

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    Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach
    (MDPI, 2024-02-04) Nicolis, Orietta; Maidana, Jean Paul; Contreras, Fabian; Leal, Danilo
    This work presents a comprehensive analysis of the economic impact of the COVID-19 pandemic, with a focus on OECD countries and the Chilean case. Utilizing a clustering approach, the research aims to investigate how countries can be categorized based on their pandemic mitigation strategies, economic responses, and infection rates. The methodology incorporates k-means and hierarchical clustering techniques, along with dynamic time warping, to account for the temporal variations in the pandemic’s progression across different nations. The study integrates the GDP into the analysis, thereby offering a perspective on the relationship between this economic indicator and health measures. Special attention is given to the case of Chile, thus providing a detailed examination of its economic and financial indicators during the pandemic. In particular, the work addresses the following main research questions: How can the OECD countries be clustered according to some health and economical indicators? What are the impacts of mitigation measures and the pension fund withdrawals on the Chilean economy? The study identifies significant differences (p-value < 0.05%) in the GDPs and infection rates between the two identified clusters that are influenced by government measures, particularly in the banking sector (55% and 60% in clusters 1 and 2, respectively). In Chile, a rebound in the IMACEC index is noted after increased liquidity, especially following partial pension fund withdrawals, thereby aligning with discrepancies between model forecasts and actual data. This study provides important insights for evidence-based public policies, thus aiding decision makers in mitigating the socioeconomic impact of global health crises and offering strategic advice for a sustainable economy.
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    Elliptical Capital Asset Pricing Models: Formulation, Diagnostics, Case Study with Chilean Data, and Economic Rationale
    (MDPI, 2023-03) Leal, Danilo; Jiménez, Rodrigo; Riquelme, Marco; Leiva, Víctor
    The capital asset pricing model (CAPM) is often based on the Gaussianity or normality assumption. However, such an assumption is frequently violated in practical situations. In this paper, we introduce the symmetric CAPM considering distributions with lighter or heavier tails than the normal distribution. These distributions are symmetric and belong to the family of elliptical distributions. We pay special attention to the family members related to the normal, power-exponential, and Student-t cases, with the power-exponential distribution being particularly considered, as it has not been explored widely. Based on these cases, the expectation-maximization algorithm can be used to facilitate the estimation of model parameters utilizing the maximum likelihood method. In addition, we derive the leverage and local influence methods to carry out diagnostics in the symmetric CAPM. We conduct a detailed case study to apply the obtained results estimating the systematic risk of the financial assets of a Chilean company with real data. We employ the Akaike information criterion to conclude that the studied models provide better results than the CAPM under Gaussianity.
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    Stochastic approach to heterogeneity in short-time announcement effects on the Chilean stock market indexes within 2016-2019
    (Taylor and Francis Ltd., 2023) Stehlík, Milan; Leal, Danilo; Kiseľák, Jozef; Leers, Joshua; Střelec, Luboš; Fuders, Felix
    We aim to examine stock market returns before and after key events in the U.S. Sino trades between 2016 and 2019. The study tracks Cumulative Abnormal Returns (CAR) of the Índice de Precio Selectivo de Acciones (IPSA or S&P/CLX IPSA is a Chilean stock market index) for 26 important events throughout this time period. By testing for both directions and significance of market reaction to said events this study aims to clarify if these events and policy announcements were sufficient to influence local equity markets, and in which direction. A simple analysis of CAR showed 16 negative reactions and 10 Positive reactions. An estimated 13 billion USD in market capitalization was lost as a result. Of the 26 events studied 18 were found to produce statistically significant reactions and 8 did not. The IPSA’s reaction to the significant events was mixed with 11 negative reactions and 7 positive reactions. We also checked for the normality of the distribution by robust normality tests and expected returns possess significant asymmetry and above-normal kurtosis. As such on aggregate it can be concluded that Chilean capital markets reacted negatively to the U.S. Sino trade war. We model IPSA in the period 2016–2022, where we can observe qualitative differences before and after 2019. To the best knowledge of the authors, the model of IPSA in this article is the first attempt in this direction. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.