Bøger af Agostino Capponi

Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard continue to plague centralized platforms, imposing societal costs. The advent of distributed ledger technologies presents an opportunity to alleviate some of the issues raised by centralized financial platforms, regardless of their integration of financial technology enhancements. These technologies have the potential to further disrupt the financial service industry by facilitating the transition to a decentralized trading environment, also referred to as decentralized finance (DeFi). DeFi enables the provision of services such as exchanges, lending, derivatives trading, and insurance without the need for a centralized intermediary. This monograph provides an overview of the DeFi ecosystem, with a focus on exchanges, lending protocols, and the decentralized governance structure in place. The monograph also discusses the operational risks inherent in the design of smart contracts and the DeFi ecosystem, and it concludes with remarks and directions for future research.

"Leveraging the research efforts of more than 60 experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and artificial intelligence. The text is structured around three main areas: "Interacting with investors and asset owners," which covers robo-advisors and price formation; "Towards better risk intermediation," which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and "Connections with the real economy," which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation behind the theory"--