Francesca Biagini - Böcker

Risk has been described in the past by a simple measure, such as the variance, and risk attitude is often considered simply a degree of risk aversion. However, this viewpoint is usually not sufficient. Risk Measures and Attitudes collects contributions which illustrate how modern approaches to both risk measures and risk attitudes are inevitably intertwined. The settings under which this is discussed include portfolio choice, mitigating credit risk and comparing risky alternatives.This book will be a useful study aid for students and researchers of actuarial science or risk management as well as practitioners.

Fractional Brownian motion (fBm) has been widely used to model a number of phenomena in diverse fields from biology to finance.

Fractional Brownian motion (fBm) has been widely used to model a number of phenomena in diverse fields from biology to finance. This huge range of potential applications makes fBm an interesting object of study. Several approaches have been used to develop the concept of stochastic calculus for fBm. The purpose of this book is to present a comprehensive account of the different definitions of stochastic integration for fBm, and to give applications of the resulting theory. Particular emphasis is placed on studying the relations between the different approaches. Readers are assumed to be familiar with probability theory and stochastic analysis, although the mathematical techniques used in the book are thoroughly exposed and some of the necessary prerequisites, such as classical white noise theory and fractional calculus, are recalled in the appendices. This book will be a valuable reference for graduate students and researchers in mathematics, biology, meteorology, physics, engineering and finance.

This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science.

This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science.