Conference: 7th Eastern Conference on Mathematical Finance

第七届东部会议在数学金融（ECMF），在北卡罗来纳州州立大学主办10月20日至22日，2023年，有助于在金融数学领域正在进行的研究和教育目标。会议在研讨会的气氛中进行，以鼓励初级和高级研究人员之间的互动。这是一个为期3天的课程，介绍该领域的最新发展，并为可能的新研究方向提供结构化讨论。本次会议将强调教学和金融数学在北卡罗来纳州的作用，该计划包括一个学生讲座和海报特别会议。支持这次会议将提高初级教师和代表性不足的群体的参与，对他们的职业能见度作出贡献，并提供与更广泛的研究人员群体互动的机会的目标。科学重点是金融风险管理，系统风险，机器学习，加密货币，区块链技术和金融数据科学的应用。2008年后，用于抵消系统性风险的监管措施，如集中清算对手方和增加资本要求，可能正在被加密货币和分散交易所区块链数据库的金融创新所规避。随着加密货币的作用在全球范围内扩大，了解去中心化金融带来的风险至关重要。总的来说，在过去的十年里，量化金融已经从简约的模型转向了更大、更复杂的机器学习模型。例如，深度神经网络的计算能力已被证明在开发用于计算金融基准的数据驱动方法方面非常有效。这些类型的量化金融技术的问题和分析是第七届ECMF的主题。会议网站地址是https://sites.google.com/view/ecmf7/homeThis奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The 7th Eastern Conference In Mathematical Finance (ECMF), hosted at North Carolina State University in October 20-22, 2023, contributes toward the ongoing research and education goals in the field of financial mathematics. The meeting is conducted with a workshop-type atmosphere to encourage interaction between junior and senior researchers. This is a 3-day program with presentations on recent developments in the field and offering structured discussion for possible new research directions. This conference will emphasize teaching and the role of financial mathematics in North Carolina, with the program including a special session for student talks and posters. Support for this conference will enhance participation from junior faculty and under-represented groups, with the goal of contributing toward their career visibility and providing an opportunity for interaction with a broader group of researchers. The scientific focus is on financial risk management, system risk, applications of machine learning, cryptocurrencies, blockchain technology, and financial data science. The post-2008 regulatory measures for counteracting systemic risk, such as centralized clearing counterparties and increased capital requirements, are perhaps being circumvented by financial innovation in blockchain databases for cryptocurrencies and decentralized exchanges. Understanding of risks posed by decentralized finance is of critical importance as the role of cryptocurrency expands globally. In general, quantitative finance in that past decade has seen a seismic shift away from parsimonious models toward the larger, more complex models of machine learning. For example, the computational power of deep neural networks has proven to be very effective in the development of data-driven methods for computing financial benchmarks. The questions and analyses of these types of quantitative finance technologies are the main theme for this 7th ECMF. The conference website address is https://sites.google.com/view/ecmf7/homeThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.