Optimal Financial Decision Making under Uncertainty

The scope of this volume is primarily to analyze from different methodological perspectives  similar valuation and optimization problems arising in financial applications, aimed at facilitating a theoretical and computational integration between methods largely regarded as alternatives. Increasingly in recent years, financial management problems such as strategic asset allocation, asset-liability management, as well as asset pricing problems, have  been presented in the literature adopting formulation and solution approaches rooted in stochastic programming, robust optimization, stochastic dynamic programming (including approximate SDP) methods, as well as policy rule optimization, heuristic approaches and others. The aim of the volume is to facilitate the comprehension of  the modeling and methodological potentials of those methods, thus their common assumptions and peculiarities, relying on similar financial problems. The volume will address different valuation problems common in finance related to: asset pricing, optimal portfolio management, risk measurement, risk control and asset-liability management.

The volume features chapters of theoretical and practical relevance clarifying recent advances in the associated applied field from different standpoints, relying on similar valuation problems and, as mentioned, facilitating a mutual and beneficial methodological and theoretical knowledge transfer. The distinctive aspects of the volume can be summarized as follows:

• Strong benchmarking philosophy, with contributors explicitly asked to underline current limits and desirable developments in their areas.
  
• Theoretical contributions, aimed at advancing the state-of-the-art in the given domain with a clear potential for applications
  
• The inclusion of an algorithmic-computational discussion of issues arising on similar valuation problems across different methods.
  
• Variety of applications: rarely is itpossible within a single volume to consider and analyze different, and possibly competing, alternative optimization techniques applied to well-identified financial valuation problems.
  
• Clear definition of the current state-of-the-art in each methodological and applied area to facilitate future research directions.

Giorgio Consigli is currently professor of applied mathematics in economics and finance at the University of Bergamo. Dr. Consigli is Coordinator of the Stochastic Programming technical section within the Italian OR society and Board Member of the European Working Groups of Stochastic Programming and Commodity and Financial Modelling within the European OR society. He is Research Fellow of the School of Mathematical Studies of the University of Cambridge (UK) and the UK Institute of Mathematics and Applications (FIMA).

He holds an honours degree in Economics at the University La Sapienza in Rome, a Diploma in Financial intermediation in the same University and a PhD in mathematics at the University of Essex in the UK.

Dr. Consigli has a substantial cooperation and R&D record with the insurance and financial industry in Italy and Internationally on the development of advanced tools for risk management and asset-liability management. Throughout the years hemaintained an active cooperation with the academic and scientific communities specifically in the areas of stochastic optimization, financial modelling, risk modelling and static and dynamic portfolio selection. He is associate editor of the J of Management Mathematics (OUP), the J of Computational Management Science (Springer), the J of Financial Engineering and Risk Management (Inderscience), Quantitative Finance Letters (Taylor and Francis).

Daniel Kuhn holds the Chair of Risk Analytics and Optimization at EPFL. Before joining EPFL, he was a faculty member at Imperial College London (2007-2013) and a postdoctoral researcher at Stanford University (2005-2006). He received a PhD in Economics from the University of St. Gallen in 2004 and an MSc in Theoretical Physics from ETH Zurich in 1999. His research interests revolve around robust optimization and stochastic programming.

Paolo Brandimarte is full professor of quantitative methods at the Department of Mathematical Sciences of Politecnico di Torino, where he teaches Financial Engineering and Business Analytics. He is also adjunct professor at ESCP Europe. His primary research interests are in the application of optimization and statistical modelling to finance and supply chain management. He has written/edited more than ten books on these subjects.