安德烈·基里连科一分钟访谈

IF 0.3 Q4 BUSINESS, FINANCE Algorithmic Finance Pub Date : 2013-01-01 DOI:10.3233/AF-13019
A. Kirilenko, A. Kirilenko
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引用次数: 0

摘要

安德烈·基里连科,麻省理工学院斯隆管理学院金融学实践教授,麻省理工学院斯隆金融与政策中心联席主任。在2013年1月加入麻省理工学院之前,基里连科在商品期货交易委员会(CFTC)工作了四年,在2010年12月至2012年12月期间担任首席经济学家。作为首席经济学家,他在使用现代分析工具和方法方面发挥了重要作用,以提高委员会在自动化金融市场中制定和执行有效监管制度的能力。基里连科最为人所知的可能是他在调查2010年5月6日的“闪电崩盘”中所扮演的角色,当时道琼斯工业平均指数在几分钟内史无前例地暴跌了近1000点,然后才最终回升。闪电崩盘最初被归咎于高频交易。根据基里连科的权威研究,高频交易并没有引发5月6日的连锁事件,但确实导致了市场的过度波动,导致整个市场系统失控。基里连科在宾夕法尼亚大学获得经济学博士学位。他的学术著作曾发表在《金融杂志》和《金融市场杂志》等杂志上,并获得无数奖项。2010年,他获得了CFTC主席卓越奖(最高荣誉),以表彰他的“非凡成就和卓越服务”。你现在的研究兴趣是什么?我的研究主要集中在由于技术进步而导致的市场设计、产品和交易策略的创新。我目前的研究兴趣是算法和高频交易,机器学习方法和模型,测量和管理系统性风险,以及创新金融产品的设计,如交易所交易基金。我着眼于伴随这些创新而来的机遇、挑战和经济激励。我还研究了它们对金融稳定造成或加剧的潜在威胁。人们经常问我:“闪电崩盘会再次发生吗?”我的回答是:是的——金融市场在技术上已经变得如此复杂和相互关联,以至于没有人真正知道整个系统是如何运作的,以及它何时会再次失灵。下一个问题通常是:监管能帮助避免这种情况吗?困难在于监管是向后看的;它总是试图解决最新的危机。事实上,我最终想开发出适合自动化时代的金融监管2.0原则。FinReg 2.0需要以网络为中心,而不是以人为中心,旨在提高安全性和弹性,鼓励创新,最重要的是,让人们重新获得对市场的信心。人们需要重新开始感觉到,金融市场是为他们的需求服务的,而不是为技术先进的“超级用户”(如高频交易员)的利益服务的。你认为什么是学术上令人兴奋的?我真的很想学习更多关于计算的原理和实践以及自动化系统的设计。我非常确信,金融体系已经变得比以往任何时候都更像一个系统,全球相互关联的交易对手和自动化的基础设施,在正常的市场条件下促进了巨大的整合,在金融危机期间迅速传播混乱。我知道有很多科学学科都在研究如何设计和操作安全可靠的自动化系统。我很想知道在这方面积累的知识是什么。幸运的是,麻省理工学院是这样做的好地方。如果你有很多时间,你会做什么?我会尽我所能帮助防止下一次系统性金融危机。上一次危机确实起源于金融体系,是美国人自大萧条以来经历的最严重的危机。800万美国人失去了工作,数百万家庭失去了家园,数千家企业倒闭。现在我想不出还有什么比弄清楚如何确保无辜的人不受金融体系的不利影响更重要的了。我对需要做什么有自己的看法。我的答案是教育。我们需要培养金融行业的下一代
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A Minute with Andrei Kirilenko
ANDREI KIRILENKO is the Professor of the Practice of Finance at the Sloan School of Management of the Massachusetts Institute of Technology (MIT) and Co-Director of the MIT Sloan Center for Finance and Policy. Prior to joining MIT in January 2013, Kirilenko spent four years at the Commodity Futures Trading Commission (CFTC) where he served as Chief Economist between December 2010 and December 2012. In his capacity as Chief Economist, he was instrumental in using modern analytical tools and methods to improve the Commission’s ability to develop and enforce an effective regulatory regime in automated financial markets. Kirilenko is perhaps best known for his role in the investigation of the “Flash Crash” of May 6, 2010, when the Dow Jones industrial average took an unprecedented plunge of almost 1000 points in minutes before ultimately recovering. The Flash Crash was originally blamed on high frequency trading. According to Kirilenko’s authoritative study, high frequency trading did not set off the chain of events on May 6, but did contribute to exorbitant market volatility as the whole market system spiraled out of control. Kirilenko received his Ph.D. in Economics from the University of Pennsylvania. His scholarly works have appeared in the Journal of Finance and the Journal of Financial Markets among others and have won numerous awards. In 2010, he was the recipient of the CFTC Chairman’s Award for Excellence (highest honor), which recognized his “extraordinary accomplishments and superior service.” What are your research interests right now? My research generally focuses on innovations in the design of markets, products, and trading strategies due to advances in technology. My current research interests are algorithmic and high frequency trading, machine-learning methods and models, measuring and managing systemic risk, and the design of innovative financial products, such as exchange traded funds. I look at the opportunities, challenges, and economic incentives that accompany these innovations. I also look at the potential threats to financial stability created or facilitated by them. People often ask me: “Could a Flash Crash happen again?” My answer is: Yes—financial markets have become so technologically complex and interconnected that no one really knows how the whole system operates and when it will malfunction again. The next question is typically: Can regulation help avoid that? The difficulty is that regulation is backward-looking; it is always trying to solve the latest crisis. In fact, I ultimately want to develop the principles of Financial Regulation 2.0 suitable for the automated era. FinReg 2.0 needs to be cyber-centric rather human-centric, designed for extra safety and resilience, encourage innovation, and, most importantly, make people regain confidence in markets. People need to start feeling again that financial markets serve their needs rather than the interests of technologically-advanced “power users” like high frequency traders. What do you see as academically exciting? I would really like to learn more about the principles and practices of computing and the design of automated systems. I have become quite convinced that the financial system has become much more of a system than ever before, with globally interconnected counterparties and automated infrastructure that facilitate tremendous integration during normal market conditions, and spread dislocation rapidly during periods of financial distress. I am aware that there are whole scientific disciplines out there on how to design and operate automated systems with safety and robustness in mind. I would really like to find out what the accumulated body of knowledge on this is. Luckily, MIT is a great place to do just that. What would you work on if you had lots of time? I would devote whatever time I have to help prevent the next systemic financial crisis. The last crisis— which did originate in the financial system—was the worst crisis that Americans have experienced since the Great Depression. Eight million Americans lost their jobs, millions of families lost their homes, and thousands of businesses got shuttered. I can’t think of anything more important right now than to figure out how to make sure that innocent people are not adversely affected by the financial system. I have a view on what needs to be done. My answer is education. We need to train the next generation of financial industry
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
期刊最新文献
Combining low-volatility and mean-reversion anomalies: Better together? Guidelines for building a realistic algorithmic trading market simulator for backtesting while incorporating market impact Graph embedded dynamic mode decomposition for stock price prediction Interest rate derivatives for the fractional Cox-Ingersoll-Ross model How smart is a momentum strategy? An empirical study of Indian equities
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