Ansgar Steland – författare

Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications.

Financial Statistics and Mathematical Finance:

Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.

Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications.

Financial Statistics and Mathematical Finance:

Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.

This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g.the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance. 

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering.

Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few.

This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

 Mathematische Modelle und Methoden sind heute in den Natur- und Biowissenschaften zu einem wichtigen Bestandteil der wissenschaftlichen Arbeit und Forschung geworden. Leitfaden der vorliegenden anschaulichen und grundlegenden Einführung in diesen Themenbereich sind die in den Naturwissenschaften typischen auftretenen Fragestellungen, anhand dessen die wichtigsten Konzepte entwickelt werden. Bei der Darstellung des Stoffes wird bewusst auf das aus der Mathematik stammende Definition-Satz-Beweis-Schema verzichtet und die Vermittlung der wesentlichen Ideen und Ansätze in den Vordergrund gestellt.

Schwerpunkte der Stoffauswahl liegen in der Wahrscheinlichkeitsrechnung, Statistik und Analysis, die einen direkten Zugang zu den wichtigen Anwendungen ermöglichen.

Wirtschaftswissenschaftler, Ingenieure und Informatiker benötigen heutzutage profunde Kenntnisse über Modelle und Methoden der angewandten Wahrscheinlichkeitstheorie und Statistik. Diese spielen eine entscheidende Rolle für das Verständnis und die theoretische Analyse komplexer Systeme wie zum Beispiel den Finanzmärkten oder dem Datenverkehr im Internet. Die vorliegende Kompakteinführung stellt die in anwendungsorientierten Lehrveranstaltungen üblichen Themen in einer modernen Form dar, die sich insbesondere an Studienanfänger richtet. Ein ausführlicher mathematischer Anhang „Mathematik kompakt“ stellt die wichtigsten Ergebnisse aus Analysis und linearer Algebra zum effizienten und leichten Nachschlagen zur Verfügung.

Für die Neuauflage wurde der Text gründlich durchgesehen und ergänzt. So wurde der Anhang „Mathematik kompakt“ erweitert und als Lernmeilensteine Fragen und Aufgaben aufgenommen, die helfen, das eigene Wissen selbstständig zu überprüfen.