The textbook presents a systematic and concise exploration of Stochastic Analysis and statistical finance, making it suitable for students in degree programs. Its rigorous yet accessible style ensures that readers can easily navigate the main topics and their interconnections, providing a self-sufficient resource for understanding complex concepts in the field.
Designed for second- or third-year undergraduate students, this book offers a concise introduction to Probability Theory, complemented by selected topics in Mathematical Statistics. It is structured for a 10- to 14-week course and assumes prior knowledge of Calculus. The text includes a variety of problems and solutions, making it ideal for weekly tutorials and reinforcing understanding of key concepts in Science, Mathematics, Statistics, Finance, or Economics.
Quantitative Methods and Empirical Rules for Incomplete Information
Exploring advanced portfolio management, this book delves into quantitative methods and empirical rules tailored for scenarios with incomplete information. It emphasizes dynamic strategies that adapt to changing market conditions, offering insights on risk assessment and asset allocation. The authors combine theoretical frameworks with practical applications, equipping readers with tools to enhance decision-making in uncertain environments. Ideal for finance professionals and students, it bridges the gap between theory and practice in investment strategies.
Dynamic Portfolio Quantitative Methods and Empirical Rules for Incomplete Information investigates optimal investment problems for stochastic financial market models. It is addressed to academics and students who are interested in the mathematics of finance, stochastic processes, and optimal control, and also to practitioners in risk management and quantitative analysis who are interested in new strategies and methods of stochastic analysis. While there are many works devoted to the solution of optimal investment problems for various models, the focus of this book is on analytical strategies based on "technical analysis" which are model-free. The technical analysis of these strategies has a number of characteristics. Two of the more important characteristics (1) they require only historical data, and (2) typically they are more widely used by traders than analysis based on stochastic models. Hence it is the objective of this book to reduce the gap between model-free strategies and strategies that are "optimal" for stochastic models. We hope that researchers, students and practitioners will be interested in some of the new empirically based methods of "technical analysis" strategies suggested in this book and evaluated via stochastic market models.