School Finance Reforms, Tax Limits, and Student Performance: Do Reforms Level Up or Dumb Down?

During the late 1970s and early 1980s, a majority of states substantially changed the ways in which schools were funded, either directly through court- or legislatively mandated school finance reform, or indirectly through tax and expenditure limits. To date, there have been few academic attempts to gauge the effects of these policy changes on actual outcomes of education. This paper is an attempt to fill this gap in the literature. We find compelling evidence that the imposition of tax or expenditure limits on local governments in a state results in a significant reduction in the mean for that state of student performance on standardized tests of mathematics skills. We also find that finance reforms in response to court mandates do not result in significant changes in either the mean level or the distribution of student performance on standardized tests of reading and mathematics. In addition, substantial finance reforms that are not legislative responses to explicit court mandates generally result in increases in mean student performance. Further, in those states that have implemented finance reforms of this type, the test performance of students residing in localities in which local revenues formed smaller shares of total revenue prior to the reforms improve relative to others after the reforms are implemented.

Mathematics of Quantitative Finance

The workshop on Mathematics of Quantitative Finance, organised at the Mathematisches Forschungsinstitut Oberwolfach from 26 February to 4 March 2017, focused on cutting edge areas of mathematical finance, with an emphasis on the applicability of the new techniques and models presented by the participants

A brief history of mathematics in finance

AbstractIn the list of possible scapegoats for the recent financial crises, mathematics, in particular mathematical finance has been ranked, without a doubt, as the first among many and quants, as mathematicians are known in the industry, have been blamed for developing and using esoteric models which are believed to have caused the deepening of the financial crisis. However, as Lo and Mueller (2010) state “Blaming quantitative models for the crisis seems particularly perverse, and akin to blaming arithmetic and the real number system for accounting fraud.” Throughout the history, mathematics and finance have always been in a close relationship. Starting from Babylonians, through Thales, and then Fibonacci, Pascal, Fermat, Bernoulli, Bachelier, Wiener, Kolmogorov, Ito, Markowitz, Black, Scholes, Merton and many others made huge contributions to the development of mathematics while trying to solve finance problems. In this paper, we present a brief historical perspective on how the development of finance theory has influenced and in turn been influenced by the development of mathematical finance theory

From Local Volatility to Local Levy Models.

We define the class of local Lévy processes. These are Lévy processes time changed by an inhomogeneous local speed function. The local speed function is a deterministic function of time and the level of the process itself. We show how to reverse engineer the local speed function from traded option prices of all strikes and maturities. The local Lévy processes generalize the class of local volatility models. Closed forms for local speed functions for a variety of cases are also presented. Numerical methods for recovery are also described.Levy processes; Derivatives securities; Random walks (mathematics); Volatility (finance); Options (finance);

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Mathematics-for-teaching: Insights from the case of annuities

Shulman’s notations of subject matter knowledge (SMK) and pedagogical content knowledge (PCK) have been very influential in education research on teachers’ knowledge for teaching. However, there is little empirical evidence in support of these as separate analytical constructs. Furthermore, attempts to distinguish SMK and PCK highlight the complex and multidimensional nature of teachers’ knowledge and hence the difficulty of separating SMK and PCK. The author adopts the notion of mathematics-for-teaching (MfT) and argues that teachers’ knowledge for teaching annuities comprises knowledge of mathematical aspects, knowledge of pedagogical aspects and contextual knowledge of finance. Drawing from a larger study in which the author taught a financial mathematics course to pre-service secondary mathematics teachers, four examples of teachers’ knowledge for teaching annuities are identified, each of which illustrates how knowledge of mathematics, knowledge of pedagogy and contextual knowledge of finance are intertwined

Systemic Risk and Default Clustering for Large Financial Systems

As it is known in the finance risk and macroeconomics literature, risk-sharing in large portfolios may increase the probability of creation of default clusters and of systemic risk. We review recent developments on mathematical and computational tools for the quantification of such phenomena. Limiting analysis such as law of large numbers and central limit theorems allow to approximate the distribution in large systems and study quantities such as the loss distribution in large portfolios. Large deviations analysis allow us to study the tail of the loss distribution and to identify pathways to default clustering. Sensitivity analysis allows to understand the most likely ways in which different effects, such as contagion and systematic risks, combine to lead to large default rates. Such results could give useful insights into how to optimally safeguard against such events.Comment: in Large Deviations and Asymptotic Methods in Finance, (Editors: P. Friz, J. Gatheral, A. Gulisashvili, A. Jacqier, J. Teichmann) , Springer Proceedings in Mathematics and Statistics, Vol. 110 2015