مدل تحلیلی برای مدلسازی مالی و مدیریت ریسک Preface: analytical models for financial modeling and risk management

Banks, financial institutions, and companies have developed financial modeling worldwide, and risk management techniques and processes have been adopted by businesses to implement risk-based policies and practices. Financial modeling and risk management are of great interest since they combine the principles of fundamental science and real-world applications. The level of sophistication of financial modeling tools, techniques, and approaches has increased considerably over the past couple of decades, driven by the experiences gained from responding to turmoil and crises, the strengthening of regulatory requirements, the adoption of new technological advances, and the introduction of new financial products and instruments. Analytical techniques are of paramount importance in financial modeling and decision-making. Operations research/management science and other related analytical disciplines are highly relevant to this area, providing the means for normative, descriptive, prescriptive, and predictive modeling. This special volume, was prepared on the occasion of the 6th International Conference of the Financial Engineering and Banking Society, which was held in Malaga, Spain, during 10–12 June 2016. It explores recent advances in the use of analytical models in all areas of financial modeling and risk management, covering both new theoretical developments and new empirical results. Extended versions of papers presented at the conference as well as other submissions were submitted to the special volume. Of 53 papers, 22 were accepted after a rigorous review process. These cover a wide range of topics, including derivatives, asset pricing, risk management, financial and banking institutions, portfolio selection, and fund management, among others. The volume starts with the paper of Consiglio, Lotfi, and Zenios, who present portfolio models for sovereign credit default swaps (CDS) to diversify idiosyncratic risk. Different investment strategies are considered for long investors, speculators, and hedgers, under a bi-objective scheme based on return and conditional value at risk (CVaR). The models are tested on data from Europe and they are found superior when compared to an established CDS index.