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In this work we have considered the study of the exchange rate series for the specific case where the formal financial market is not active. In those situations, we would be interested in the parallelization of the exchange rate with financial indexes for stabilized financial market. We observed that the stationarity of the distribution for some the exchange rate of currencies traded in the country differs significantly. The time dynamics shows the presence of the elements of local critical behavior, but those tendencies attenuate and fade away in an a periodic fashion. Next, we considered and evidenced the correlation distances and dissimilarity between exchange rates of national currencies versus euro and dollar and golden prices. It resulted that two exchange rates do have different distance from golden price taken for references. The correlation distance between the series of the return in different period has evidenced that there is not a regular behavior in this respect.

This article examines the integration of financial numeracy in secondary mathematics textbooks. It addresses the gap in literature on how financial concepts are portrayed in textbooks, contributing to the establishment of financial numeracy as a field of research and practice. The study analyzed financial numeracy tasks in three published textbook collections from the Canadian Province of Quebec, using qualitative data software to code tasks by collection, grade, mathematical domain, and financial numeracy approach. The results revealed a higher frequency of financial tasks in early secondary grades compared to late (streamed) grades, with a shift in focus from contextual to conceptual approaches in later grades. Algebra and arithmetic domains contained most financial tasks, with significant differences among textbook collections. The findings suggest a need for textbooks to balance mathematical and financial aspects in tasks, and for teachers to receive support in content and pedagogy related to financial numeracy. The study advocates for a nuanced understanding of financial concepts in mathematics, approaching financial numeracy as sensemaking in financial situations (which goes solving problems with defined variables for decision making).

This paper has as its main theme the fitting in practice of the variance-gamma distribution, which allows for skewness, by moment methods. This fitting procedure allows for possible dependence of increments in log returns, while retaining their stationarity. It is intended as a step in a partial synthesis of some ideas of Madan, Carr and Chang (1998) and of Heyde (1999). Standard estimation and hypothesis-testing theory depends on a large sample of observations which are independently as well as identically distributed and consequently may give inappropriate conclusions in the presence of dependence.

The training of highly skilled persons for the areas of Economics and Commerce requires that degree students upon entry need certain knowledge and skills in mathematics including aspects of statistics. The context is distance education and entry students seem unable to cope with the requirements of the mathematics-based topics that they need to study at the first year of tertiary education. It resulted in university staff speculating about a gap in pre-knowledge and skills. This study aims to investigate this phenomenon, starting with content and Revised Bloom’s Taxonomy analyses of final year school examination papers in mathematics, comparing it with similar analyses of first-year mathematics-based module examinations in the Economic sciences. Students that passed mathematics at school are supposed to have had adequate preparation for the first level Bachelor of Commerce. Coping with routine procedures mainly upon exit from secondary education does not signal well for the subsequent training of economists and commerce students. The situation seemingly does not improve at tertiary level where there is a further emphasis on routine procedures compared to higher order thinking skills.

'Introductory Mathematical Analysis for Quantitative Finance' is a textbook designed to enable students with little knowledge of mathematical analysis to fully engage with modern quantitative finance. A basic understanding of Calculus and Linear Algebra is assumed. The exposition of the topics is as concise as possible, since the chapters are intended to represent a preliminary contact with the mathematical concepts used in Quantitative Finance. The aim is that this book can be used as a basis for an intensive one-semester course. Features Written with applications in mind while maintaining mathematical rigor. Suitable for undergraduate or master's level students with an Economics or Management background. Complemented with various solved examples and exercises, to support the understanding of the subject.

The training of highly skilled persons for the areas of Economics and Commerce requires that degree students upon entry need certain knowledge and skills in mathematics including aspects of statistics. The context is distance education and entry students seem unable to cope with the requirements of the mathematics-based topics that they need to study at the first year of tertiary education. It resulted in university staff speculating about a gap in pre-knowledge and skills. This study aims to investigate this phenomenon, starting with content and Revised Bloom’s Taxonomy analyses of final year school examination papers in mathematics, comparing it with similar analyses of first-year mathematics-based module examinations in the Economic sciences. Students that passed mathematics at school are supposed to have had adequate preparation for the first level Bachelor of Commerce. Coping with routine procedures mainly upon exit from secondary education does not signal well for the subsequent training of economists and commerce students. The situation seemingly does not improve at tertiary level where there is a further emphasis on routine procedures compared to higher order thinking skills.

Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptos’ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and people’s attitudes toward them. Based on finance theory, these differing characteristics could affect cryptos’ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.

This paper presents a long-term study of Preservice Mathematics Teachers (PMTs) at the Faculty of mathematics, physics and informatics, Comenius University in Bratislava (FMFI UK), focusing on the implementation of digital technologies (DT) into the teaching of theoretical and practical (or applied) subjects. We conducted parallel research into two aspects, one on Calculus lessons as a theoretical subject, another on the Financial Mathematics module as an applied subject. The implementation of DT and the way this was measured varied from year to year and also in the method of implementation into the aforementioned subjects. The methods of implementation and the results are briefly described, and a comparison of these two subjects in the PMTs’ preparation is also discussed.

This is an attempt to review some of the breakthroughs in economic research as they impacted the nascent field of financial mathematics over the last 25 years. Because of the prominent role of Finance and Stochastics in the definition of this emerging field, I try to view things through the lens of its published papers, and I try to stay away from financial engineering applications.

Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, SGAN, which uses generative adversarial networks (GANs) to extract the nonlinear latent structure from eight sentiment proxies from February 2003 to September 2023 in the Chinese A-share market. Unlike traditional linear dimensionality reduction methods, GANs are able to capture complex market dynamics through adversarial training, effectively reducing noise and improving prediction accuracy. The empirical analyses show that SGAN significantly outperforms existing methods in both in-sample and out-of-sample prediction capabilities. The GAN-based investment strategy achieves impressive annualized returns and provides a powerful tool for portfolio construction and risk management. Robustness tests across economic cycles, industries, and U.S. markets further validate the stability of SGAN. These findings highlight the unique advantages of GANs as sentiment-driven financial forecasting tools, providing market participants with new ways to more accurately capture sentiment-shifting trends and develop effective investment strategies.