Persona: Arguedas Sanz, Raquel
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Arguedas Sanz
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Publicación Burden of Hospitalizations Related to Pneumococcal Infection in Spain (2016–2020)(MDPI, 2023-01-14) Gil Prieto, Ruth; Allouch, Nizar; Jimeno, Isabel; Hernández Barrera, Valentín; Gil de Miguel, Ángel; Arguedas Sanz, RaquelPneumococcal infection strongly contributes to morbidity and mortality in Spain. A total of 253,899 hospitalizations related to pneumococcal infection occurred from 2016 to 2020. Fifty-eight percent were men, the mean age was 67 years old, and the average length of hospitalization was 12.72 days. The annual hospitalization rate was 10.84 hospitalizations per 10,000 population, increasing significantly with age, reaching 65.75 per 10,000 population in those aged >85 years. The hospitalization rates for pneumococcal pneumonia, sepsis, and meningitis were 2.91, 0.12, and 0.08 hospitalizations per 10,000, respectively, and reached the highest value in those aged >85 for pneumococcal pneumonia and sepsis, with 22.29 and 0.71 hospitalizations per 10,000, respectively, and in children up to 1 year old for pneumococcal meningitis, with 0.33 hospitalizations per 10,000. The total number of deaths during the study period was 35,716, with a case-fatality rate of 14.07%. For pneumococcal pneumonia, sepsis, and meningitis, the case-fatality rates were 8.47%, 23.71%, and 9.99%, respectively. The case-fatality rate increased with age and did not vary by sex. The annual cost of these hospitalizations was more than EUR 359 million. There is therefore a high burden of disease and mortality caused by pneumococcal infection in our country, especially in elderly individuals.Publicación Efficiency in cryptocurrency markets: new evidence(Springer, 2021-07-26) López Martín, Carmen; Benito Muela, Sonia; Arguedas Sanz, RaquelIn this paper we carried out a comprehensive study of the efficiency in the cryptocurrency markets. The markets under study are: Bitcoin, Litecoin, Ethereum, Ripple, Stellar and Monero. To studdy the efficiency of these markets, we use a set of five test which are applied in both a static context and dynamic context. The results obtained depend on both the analysis period and the methodology used to test the predictability of the return. However, some conclusions can be drawn: first, we observe that overall, the efficiency degree tends to increase with the time. Second, although the efficiency market seems to change along the period, the changes in the Bitcoin, Litecoin and Ethereum market show a clear tendency that evolves from less to more efficiency. In the case of Ripple, Stellar and Monero, periods of efficiency alternate with periods of inefficient, which is consistent with the Adaptive Market Hypothesis.Publicación The extreme temperature factor in asset pricing models: Evidence from Europe(ELSEVIER, 2024-08) González Sánchez, Mariano; Arguedas Sanz, Raquel; Segovia San Juan, Ana IsabelGrowing concern about climate change has led to increased research into the effects of climate on markets. One of the weather variables studied is temperature. The previous studies considered that the temperature influences on asset returns through changes in investor mood. There are few studies that incorporate a risk factor to analyze the effects of temperature changes on asset returns. We extract positive and negative extreme temperature changes to design three temperature factors. By a cross-section asset pricing model, we find evidence that temperature shocks (hot and cold) show a significant monthly risk premium and skewness for temperature changes.Publicación Climate transition risk in determining credit risk: evidence from firms listed on the STOXX Europe 600 index(Springer, 2023-04-10) Ramos García, Daniel; López Martín, Carmen; Arguedas Sanz, RaquelThis paper assesses whether a climate factor is relevant to measure default risk in a sample of main companies listed on the STOXX Europe 600 exchange from 2010 to 2020. The starting point is a factorial panel datamodel which is subsequently modified to capture the climate impact through different functional forms.We find that relevant differences in default risk exist before and after the Paris Agreement. Our analysis also indicates that this difference cannot be explained by means of traditional financial factors. Finally, we further show that a climate change risk and opportunities label is a significant factor in evaluating credit risk, both prior to and post-Paris agreement. These results are important to the extent that they suggest that companies’ market performance itself allows to measure differences in credit risk between companies and to link them with climate risk factors. This approach may be useful as a complement or in combination with the traditional use of exogenous climate factors that have been widely used in the literature in this field.Publicación A comparison of market risk measures from a twofold perspective: accurate and loss function(Elsevier, 2023-06-04) Benito Muela, Sonia; López Martín, Carmen; Arguedas Sanz, RaquelUnder the new regulation based on Basel solvency framework, known as Basel III and Basel IV, financial institutions must calculate the market risk capital requirements based on the Expected Shortfall (ES) measure, replacing the Value at Risk (VaR) measure. In the financial literature, there are many papers dedicated to compare VaR approaches but there are few studies focusing in comparing ES approaches. To cover this gap, we have carried out a comprenhensive comparative of VaR and ES models applied to IBEX-35 stock index. The comparison has been carried out from a twofold perspective: accurate risk measure and loss functions. The results indicate that the method based on the conditional Extreme Value Theory (EVT) is the best in estimating market risk, outperforming Parametric method and Filter Historical Simulation.Publicación Models used to characterise blockchain features. A systematic literature review and bibliometric analysis(Elsevier, 2023-05-01) Rico Peña, Juan Jesús; Arguedas Sanz, Raquel; López Martín, CarmenBlockchain has emerged as an innovative technology with potential to transform business management, through operational efficiency improvements. Nevertheless, several performance and vulnerability issues have been identified for the different typologies supporting the wide range of blockchain-based applications currently implemented in different domains. A variety of analytical and empirical models are being used to evaluate the issues associated with the different blockchain typologies, enabling systematic analyses of the corresponding efficiency impact, and technical or economic threats. A thorough systematic literature review of these models has been performed, followed by a detailed assessment on the way these models have been employed, and the target parameters and applications evaluated (336 research selected and analysed). We propose a co-classification of these models, allowing us to identify which ones are employed to a greater extent to address the different blockchain issues in scientific research. In a second step, a bibliometric analysis on the selected research is conducted, offering a complementary overview of the status of and trends in blockchain modelling, including the most prolific authors and leading contributing countries to the topic. The main outcome and contribution of the paper is the provision of a broad overview on how blockchain issues have been analytically tackled, through the synthesis and meta-analysis of the models used in the scientific literature since the inception of blockchain technology. The results have two main direct applications, firstly supporting novel vulnerability and performance analyses of existing blockchain applications by providing historical information on the models used so far, as well as the key parameters and typology of the blockchain-based applications evaluated. Secondly, in the implementation of new applications, by allowing the recognition of key issues identified that are associated with the different blockchain typologies and to determine the most suitable models to analyse the weaknesses and risks of the alternative designs under evaluation for these new implementations.Publicación Analysis of the Influence of the Moment the Internationalization Process Begins on the Internationalization Intensity of Family and Nonfamily Businesses: An Approach Using a Tobit Model(MDPI, 2022-10-10) Varas Fuente, Oscar Javier; Arguedas Sanz, Raquel; Rodrigo Moya, BeatrizThe specific characteristics of family businesses as well as the internationalization path followed can influence the intensity of the internationalization process. Many studies have analyzed how family character can influence the internationalization process of family businesses, and the results obtained have not been conclusive. Nevertheless, previous research has not sufficiently addressed the influence that the moment of initiation of the internationalization process has on the levels of internationalization achieved. Based on the behavioral agency model, the unique set of business resources (familiness), and the socioemotional wealth (SEW) perspective, this study examines, the internationalization intensity of family and nonfamily businesses in two defined groups (early internationalization and internationalization from the local market). Likewise, the effect that the entry of the second generation has on the internationalization of these companies is analyzed. To perform this analysis, Tobit regression models are estimated from a data set of panel data from the Spanish Survey on Business Strategies for small and medium-sized Spanish family businesses from 2005 to 2016, finding that family ownership and management have a negative influence on the intensity of exports, regardless of the path of internationalization followed, and that the entry of new generations has a positive relationship with the level of internationalization of these businesses. Finally, implications of the findings for research and management are discussed.Publicación Tackling the challenge of peer learning in hybrid and online universities(Springer, 2022-10-20) Mendieta Aragón, Adrián; Arguedas Sanz, Raquel; Ruiz Gómez, Luis Manuel; Navío Marco, JulioPeer learning is not fully developed or researched in online and hybrid higher education. This research analyses a peer learning experience in the asynchronous part of hybrid teaching, in one of the largest blended universities in Europe, promoting students to act as teachers of their peers, by preparing digital content (videos) for the course. This article studies whether there are behaviour patterns and different perceptions associated between students who act as teachers, and those who only act as students. The results indicate, among other findings, that students demand this type of activities, and value them very positively. Specifically, the “teachers” consider that this activity increases their motivation for the subject and their performance; they also consider that it significantly improves their creativity and communication skills, and they would definitely participate in the project again. The assessment of the students who merely view the materials is also very positive, and they prefer a learning method through classmate videos than the traditional learning method with printed materials. The research is also a boost to finding ways to promote learning among equals in non-classroom teaching in digital environmentsPublicación Temporal optimisation of signals emitted automatically by securities exchange indicators(Universidad del País Vasco UPV/EHU, 2020-11-27) Ventura Pérez, Enrique; Martín García, Rodrigo; Arguedas Sanz, RaquelStock exchange indicators deliver buy/sell signals that enable analysts to improve the results of a strategy based strictly on fundamental analysis. Nonetheless, since the automatic implementation of signals as they appear may not yield optimal returns, the present paper analysed the suitability of using a series of technical indicators as guidance for portfolio results. A second aim pursued was to study how delaying the implementation of indicator signals may enhance profitability. A simulation was performed for the years 2005-2016 using the most representative index for the Spanish stock exchange, the IBEX35 and all its constituent securities, along with seven indicators (RoC, RSI, SMA, EMA, MACD, Bollinger bands and Stochastic Oscillator) and a total of 81 combinations of buy/sell lag times. The definition of three non-overlapping sub-periods to guarantee the reliability of the findings yielded a total of 61 236 simulated portfolios. The conclusion drawn from the results was that for certain combinations of indicators, delaying the implementation of buy/sell signals improves returns. More specifically, optimal lag times identified for RSI and EMA signals were shown to deliver statistically significant improvements in portfolio returns, irrespective of the period studied. Those findings were consistent the results of an alternative simulation in which the five securities that were both the most liquid and had the greatest impact on the index were not considered, to rule out the possible effect of the relative weight of securities on either portfolio returns or their normalisationPublicación A cryptocurrency empirical study focused on evaluating their distribution functions(Elsevier, 2022-02-14) López Martín, Carmen; Arguedas Sanz, Raquel; Benito Muela, SoniaThis paper thoroughly examines the statistical properties of cryptocurrency returns, particularly focusing on studying which is the best statistical distribution for fitting this type of data. The preliminary statistical study reveals (i) high volatility, (ii) an inverse leverage effect, (iii) skewed distributions and (iv) high kurtosis. To capture the nonnormal characteristics observed in cryptocurrency data, we verified the goodness of fit of a large set of distributions, both symmetric and skewed distributions such as skewed Student-t, skewed generalized t, skewed generalized error and the inverse hyperbolic sign distributions. The results show that the skewed distributions outperform normal and Student-t distributions in fitting cryptocurrency data, although there is no one skewed distribution that systematically better fits the data. In addition, we compare these distributions in terms of their ability to forecast the market risk of cryptocurrencies. In line with the results obtained in the statistical analysis, we find that the skewed distributions provide better risk estimates than the normal and Student-t distributions, both in short and long positions, with SGED being the distribution that provides better results.