Francesca Biagini (born 31 July 1973) is a German and Italian mathematician [1] specializing in mathematical finance, stochastic calculus, and probability theory. Topics in her research include fractional Brownian motion and portfolio optimization for inside traders. [2] She is a professor of applied mathematics and vice president for international affairs and diversity at Ludwig Maximilian University of Munich, [1] [3] and president of the Bachelier Finance Society. [4]
Biagini was a high school student in Pistoia, and earned a laurea in mathematics in 1996 from the University of Pisa, under the mentorship of Margherita Galbiati. [1] She completed a doctorate in 2001 at the Scuola Normale Superiore di Pisa, with the dissertation Quadratic hedging approach for interest rate models with stochastic volatility supervised by Maurizio Pratelli. [1] [5]
She worked as an assistant professor at the University of Bologna from 1999 to 2005, when she moved to Ludwig Maximilian University of Munich as an associate professor. After declining an offer to become a chaired professor at the University of Hanover in 2008, she was given a chair as full professor of applied mathematics at the University of Munich in 2009. [1]
She became vice president for international affairs and diversity at the University of Munich, and is president of the Bachelier Finance Society for the 2020–2021 term. [1]
With Massimo Campanino, Biagini is the coauthor of Elementi di Probabilità e Statistica (Springer, 2005), an Italian-language textbook on probability theory and statistics translated into English as Elements of Probability and Statistics: Introduction to Probability with the De Finetti's Approach and to Bayesian Statistics (Springer, 2016). [6] With Yaozhong Hu, Bernt Øksendal, and Tusheng Zhang, she is the coauthor of the monograph Stochastic Calculus for Fractional Brownian Motion and Applications (Springer, 2008). [7]
Biagini was awarded the Princess Therese of Bavaria Prize, an award for outstanding women scientists at Ludwig Maximilian University of Munich, in 2019. [8]
In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a family of random variables. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. Examples include the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule. Stochastic processes have applications in many disciplines such as biology, chemistry, ecology, neuroscience, physics, image processing, signal processing, control theory, information theory, computer science, cryptography and telecommunications. Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance.
Stochastic calculus is a branch of mathematics that operates on stochastic processes. It allows a consistent theory of integration to be defined for integrals of stochastic processes with respect to stochastic processes. This field was created and started by the Japanese mathematician Kiyoshi Itô during World War II.
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