An exclusive brief interview by Algorithmic Finance with Peter Bossaerts.
算法财经对Peter Bossaerts的独家简短采访。
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An exclusive brief interview by Algorithmic Finance with Andrew Odlyzko.
Algorithmic Finance对Andrew Odlyzko的独家简短采访。
{"title":"A Minute with Andrew Odlyzko","authors":"Algorithmic Finance Journal","doi":"10.3233/af-140046","DOIUrl":"https://doi.org/10.3233/af-140046","url":null,"abstract":"An exclusive brief interview by <i>Algorithmic Finance</i> with Andrew Odlyzko.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/af-140046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bryant Chen, William W. Y. Hsu, Jan-Ming Ho, M. Kao
This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we introduce fractional-step lattices to help reduce interpolation error in the extrapolation algorithms. We demonstrate the efficiency and accuracy of these algorithms with numerical results. A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.
{"title":"Linear-Time Accurate Lattice Algorithms for Tail Conditional Expectation","authors":"Bryant Chen, William W. Y. Hsu, Jan-Ming Ho, M. Kao","doi":"10.3233/AF-140034","DOIUrl":"https://doi.org/10.3233/AF-140034","url":null,"abstract":"This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we introduce fractional-step lattices to help reduce interpolation error in the extrapolation algorithms. We demonstrate the efficiency and accuracy of these algorithms with numerical results. A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2014-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-140034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with Kenneth J. Arrow. Kenneth J. Arrow is the Joan Kenney Professor of Economics and Professor of Operations Research, emeritus; a CHP/PCOR fellow; and an FSI senior fellow by courtesy. He is a Nobel Prize-winning economist whose work has been primarily in economic theory and operations research, focusing on areas including social choice theory, risk bearing, medical economics, general equilibrium analysis, inventory theory, and the economics of information and innovation. He was one of the first economists to note the existence of a learning curve, and he also showed that under certain conditions an economy reaches a general equilibrium. In 1972, together with Sir John Hicks, he won the Nobel Prize in economics for his pioneering contributions to general equilibrium theory and welfare theory. To date, he is still the youngest person ever to receive that award. Arrow has served on the economics faculties of the University of Chicago, Harvard and Stanford. Prior to that, he served as a weather officer in the U.S. Air Corps (1942–1946), and a research associate at the Cowles Commission for Research in Economics (1947–1949). In addition to the Nobel Prize, he has received the American Economic Association’s John Bates Clark Medal and was a recipient of the 2004 National Medal of Science, presented by President George W. Bush for his contributions to research on the problem of making decisions using imperfect information and his research on bearing risk. He is a member of the National Academy of Sciences and the Institute of Medicine. He received a BS from City College, an MA and PhD from Columbia University, and holds approximately 20 honorary degrees.
在每一期中，Algorithmic Finance都会对我们的顾问或编辑委员会的一位成员或另一位领先的学者或从业者进行简短的采访。这些简短的谈话旨在提供他们当前想法的一瞥。在这一期，我们与肯尼斯J.阿罗谈话。肯尼斯·j·阿罗是琼·肯尼经济学和运筹学名誉教授;CHP/PCOR研究员;还是FSI的高级研究员他是诺贝尔经济学奖得主，主要从事经济理论和运筹学研究，研究领域包括社会选择理论、风险承担、医学经济学、一般均衡分析、库存理论以及信息和创新经济学。他是最早注意到学习曲线存在的经济学家之一，他还表明，在某些条件下，经济会达到一般均衡。1972年，他因在一般均衡理论和福利理论方面的开创性贡献，与约翰•希克斯爵士(Sir John Hicks)一起获得诺贝尔经济学奖。到目前为止，他仍然是获得该奖项最年轻的人。阿罗曾任职于芝加哥大学、哈佛大学和斯坦福大学经济系。在此之前，他曾在美国空军担任气象官(1942-1946)，并在考尔斯经济研究委员会担任研究员(1947-1949)。除了诺贝尔奖，他还获得了美国经济协会的约翰·贝茨·克拉克奖章，并获得了2004年由乔治·w·布什总统颁发的国家科学奖章，以表彰他对利用不完全信息做出决策问题的研究以及他对承担风险的研究的贡献。他是美国国家科学院和医学研究所的成员。他获得了城市学院的学士学位，哥伦比亚大学的硕士和博士学位，并拥有大约20个荣誉学位。
{"title":"A Minute with Kenneth J. Arrow","authors":"Philip Z. Maymin","doi":"10.3233/AF-140035","DOIUrl":"https://doi.org/10.3233/AF-140035","url":null,"abstract":"In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with Kenneth J. Arrow. Kenneth J. Arrow is the Joan Kenney Professor of Economics and Professor of Operations Research, emeritus; a CHP/PCOR fellow; and an FSI senior fellow by courtesy. He is a Nobel Prize-winning economist whose work has been primarily in economic theory and operations research, focusing on areas including social choice theory, risk bearing, medical economics, general equilibrium analysis, inventory theory, and the economics of information and innovation. He was one of the first economists to note the existence of a learning curve, and he also showed that under certain conditions an economy reaches a general equilibrium. In 1972, together with Sir John Hicks, he won the Nobel Prize in economics for his pioneering contributions to general equilibrium theory and welfare theory. To date, he is still the youngest person ever to receive that award. Arrow has served on the economics faculties of the University of Chicago, Harvard and Stanford. Prior to that, he served as a weather officer in the U.S. Air Corps (1942–1946), and a research associate at the Cowles Commission for Research in Economics (1947–1949). In addition to the Nobel Prize, he has received the American Economic Association’s John Bates Clark Medal and was a recipient of the 2004 National Medal of Science, presented by President George W. Bush for his contributions to research on the problem of making decisions using imperfect information and his research on bearing risk. He is a member of the National Academy of Sciences and the Institute of Medicine. He received a BS from City College, an MA and PhD from Columbia University, and holds approximately 20 honorary degrees.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"3 1","pages":"1-2"},"PeriodicalIF":0.5,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-140035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kesheng Wu, E. Bethel, Ming Gu, D. Leinweber, O. Rübel
Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. This requires computing resources that are not easily available to financial academics and regulators. Fortunately, data-intensive scientific research has developed a series of tools and techniques for working with a large amount of data. In this work, we demonstrate that these techniques are effective for market data analysis by computing an early warning indicator called Volume-synchronized Probability of Informed trading (VPIN) on a massive set of futures trading records. The test data contains five and a half year’s worth of trading data for about 100 most liquid futures contracts, includes about 3 billion trades, and takes 140GB as text files. By using (1) a more efficient file format for storing the trading records, (2) more effective data structures and algorithms, and (3) parallelizing the computations, we are able to explore 16,000 different parameter combinations for computing VPIN in less than 20 hours on a 32-core IBM DataPlex machine. On average, computing VPIN of one futures contract over 5.5 years takes around 1.5 seconds on one core, which demonstrates that a modest computer is sufficient to monitor a vast number of trading activities in real-time – an ability that could be valuable to regulators. By examining a large number of parameter combinations, we are also able to identify the parameter settings that improves the prediction accuracy from 80% to 93%.
{"title":"A Big Data Approach to Analyzing Market Volatility","authors":"Kesheng Wu, E. Bethel, Ming Gu, D. Leinweber, O. Rübel","doi":"10.2139/ssrn.2274991","DOIUrl":"https://doi.org/10.2139/ssrn.2274991","url":null,"abstract":"Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. This requires computing resources that are not easily available to financial academics and regulators. Fortunately, data-intensive scientific research has developed a series of tools and techniques for working with a large amount of data. In this work, we demonstrate that these techniques are effective for market data analysis by computing an early warning indicator called Volume-synchronized Probability of Informed trading (VPIN) on a massive set of futures trading records. The test data contains five and a half year’s worth of trading data for about 100 most liquid futures contracts, includes about 3 billion trades, and takes 140GB as text files. By using (1) a more efficient file format for storing the trading records, (2) more effective data structures and algorithms, and (3) parallelizing the computations, we are able to explore 16,000 different parameter combinations for computing VPIN in less than 20 hours on a 32-core IBM DataPlex machine. On average, computing VPIN of one futures contract over 5.5 years takes around 1.5 seconds on one core, which demonstrates that a modest computer is sufficient to monitor a vast number of trading activities in real-time – an ability that could be valuable to regulators. By examining a large number of parameter combinations, we are also able to identify the parameter settings that improves the prediction accuracy from 80% to 93%.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2013-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2139/ssrn.2274991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68053130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work's main purpose is to understand the price dynamics in a generic limit order market, and illustrate a dynamical trading mechanism that can be applied to explore its market microstructure. First and foremost, we capture the iterative nature of the limit order market, and quantitatively identify its capacities as a means to develop switching schemes for the appearances of different sorts of traders. After formally introducing a dynamical trading system to replace the complex limit order market, we then study trading processes in that trading system from both deterministic and stochastic perspectives, in the purpose of recognizing conditions of general instability and stochastic stability in the trading system. In the final part of this work, the dynamics of the spread and mid-price in a controlled trading system will be investigated, which fairly serves to verify the robustness of stochastic stability appearing in an uncontrolled trading system.
{"title":"Dynamical Trading Mechanisms in Limit Order Markets","authors":"Shilei Wang","doi":"10.3233/AF-13027","DOIUrl":"https://doi.org/10.3233/AF-13027","url":null,"abstract":"This work's main purpose is to understand the price dynamics in a generic limit order market, and illustrate a dynamical trading mechanism that can be applied to explore its market microstructure. First and foremost, we capture the iterative nature of the limit order market, and quantitatively identify its capacities as a means to develop switching schemes for the appearances of different sorts of traders. After formally introducing a dynamical trading system to replace the complex limit order market, we then study trading processes in that trading system from both deterministic and stochastic perspectives, in the purpose of recognizing conditions of general instability and stochastic stability in the trading system. In the final part of this work, the dynamics of the spread and mid-price in a controlled trading system will be investigated, which fairly serves to verify the robustness of stochastic stability appearing in an uncontrolled trading system.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69722953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A minute with Marcos Lopez de Prado","authors":"Philip Z. Maymin","doi":"10.3233/AF-13029","DOIUrl":"https://doi.org/10.3233/AF-13029","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"2 1","pages":"167-168"},"PeriodicalIF":0.5,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.
{"title":"Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing","authors":"N. Fogarasi, J. Levendovszky","doi":"10.3233/AF-13026","DOIUrl":"https://doi.org/10.3233/AF-13026","url":null,"abstract":"We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69722901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 t
{"title":"A Minute with Andrei Kirilenko","authors":"A. Kirilenko, A. Kirilenko","doi":"10.3233/AF-13019","DOIUrl":"https://doi.org/10.3233/AF-13019","url":null,"abstract":"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 t","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"2 1","pages":"1-2"},"PeriodicalIF":0.5,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with Giovanni Barone-Adesi. – GIOVANNI BARONE-ADESI is professor of finance theory and director at the Swiss Finance Institute, University of Lugano, Switzerland. He studied electrical engineering as an undergraduate at the University of Padova. Later he received a MBA and a PhD from the Graduate Business School at the University of Chicago, specializing in Finance and Statistics. Before moving to Lugano he has taught at the University of Alberta, University of Texas at Austin, the Wharton School of the University of Pennsylvania and City University. His main research interests are derivative securities, asset and risk management. He is the author of several models for valuing and hedging securities. Especially well-known are his contributions with Whaley to the pricing of American commodity options and his filtered simulation approach to the measurement of market risk, developed while advising the London Clearing House. His more recent works concern the pricing of index options, barrier options, and gold derivatives. Currently he is president of Open Capital, a fund management firm. He has been an advisor to several exchanges, financial intermediaries and other business organizations in the areas of risk management and financial strategy. – What are your research interests right now? Currently I am interested in understanding the component of systemic risk due to investors’ behavior, which has been neglected in the literature. Most of the current debate on financial regulation relates to institutions too big to fail. I am old enough to remember the Savings & Loans crisis. The herding behavior of thousands of small institutions may be very destructive. I am trying to model investors’ behavior by proposing a precise definition of sentiment that is econometrically testable, linked to the pricing kernel. Also I am interested in the herding generated by regulation itself. Rules meant to increase safety may force institutions toward holding similar portfolios and also to attempt making simultaneous adjustments. This behavior increases the likelihood of a crash. What do you see as academically exciting? I enjoy financial research because it brings together ideas from a wide variety of academic disciplines. Some of the most innovative theories I am excited about are based on the analysis of the stability of complex systems, the neurological basis of investment decisions, and the legal foundations of financial systems. On a more applied level, I am interested in the design of a new financial architecture to replace the economic functions of the financial system displaced by the new safety regulations. Whenever we say that banks should reduce their exposure to a market, we leave unanswered q
在每一期中，Algorithmic Finance都会对我们的顾问或编辑委员会的一位成员或另一位领先的学者或从业者进行简短的采访。这些简短的谈话旨在提供他们当前想法的一瞥。本期，我们将与乔瓦尼·巴隆-阿德西进行对话。- GIOVANNI BARONE-ADESI，金融理论教授，瑞士卢加诺大学瑞士金融研究所所长。他本科在帕多瓦大学学习电气工程。后来，他获得了芝加哥大学研究生商学院的工商管理硕士学位和博士学位，专攻金融和统计。在搬到卢加诺之前，他曾在阿尔伯塔大学、德克萨斯大学奥斯汀分校、宾夕法尼亚大学沃顿商学院和城市大学任教。主要研究方向为衍生证券、资产与风险管理。他是几个证券估值和对冲模型的作者。尤其著名的是他与惠利对美国商品期权定价的贡献，以及他在为伦敦结算所提供咨询服务期间提出的市场风险衡量的过滤模拟方法。他最近的著作涉及指数期权、障碍期权和黄金衍生品的定价。目前，他是Open Capital(一家基金管理公司)的总裁。他曾担任多家交易所、金融中介机构和其他商业组织在风险管理和金融战略领域的顾问。-你现在的研究兴趣是什么?目前我感兴趣的是理解由投资者行为引起的系统性风险的组成部分，这在文献中被忽视了。当前关于金融监管的辩论，大多与那些“大到不能倒”的机构有关。我年纪大了，还记得储贷危机。成千上万的小机构的羊群行为可能是非常具有破坏性的。我试图为投资者的行为建模，方法是对情绪给出一个精确的定义，这个定义在计量经济学上是可检验的，与定价核心相关。此外，我对监管本身产生的羊群效应也很感兴趣。旨在提高安全性的规则可能会迫使机构持有类似的投资组合，并试图同时进行调整。这种行为增加了崩溃的可能性。你认为什么是学术上令人兴奋的?我喜欢金融研究，因为它汇集了各种学科的思想。令我兴奋的一些最具创新性的理论是基于对复杂系统稳定性的分析、投资决策的神经学基础和金融系统的法律基础。在更实用的层面上，我感兴趣的是设计一种新的金融架构，以取代被新的安全法规取代的金融体系的经济功能。每当我们说银行应该减少对市场的敞口时，我们就没有回答将取代它们的金融中介机构的问题。如果你有很多时间，你会做什么?我希望设计一个更安全的金融体系，以支持全球经济。我不相信仅仅通过填补现有的差距就能实现这一目标。这将是未来十年的挑战。然而，现在我把所有的时间都花在了更紧迫的事情上。
{"title":"A Minute with Giovanni Barone-Adesi","authors":"G. Barone-Adesi","doi":"10.3233/AF-13024","DOIUrl":"https://doi.org/10.3233/AF-13024","url":null,"abstract":"In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with Giovanni Barone-Adesi. – GIOVANNI BARONE-ADESI is professor of finance theory and director at the Swiss Finance Institute, University of Lugano, Switzerland. He studied electrical engineering as an undergraduate at the University of Padova. Later he received a MBA and a PhD from the Graduate Business School at the University of Chicago, specializing in Finance and Statistics. Before moving to Lugano he has taught at the University of Alberta, University of Texas at Austin, the Wharton School of the University of Pennsylvania and City University. His main research interests are derivative securities, asset and risk management. He is the author of several models for valuing and hedging securities. Especially well-known are his contributions with Whaley to the pricing of American commodity options and his filtered simulation approach to the measurement of market risk, developed while advising the London Clearing House. His more recent works concern the pricing of index options, barrier options, and gold derivatives. Currently he is president of Open Capital, a fund management firm. He has been an advisor to several exchanges, financial intermediaries and other business organizations in the areas of risk management and financial strategy. – What are your research interests right now? Currently I am interested in understanding the component of systemic risk due to investors’ behavior, which has been neglected in the literature. Most of the current debate on financial regulation relates to institutions too big to fail. I am old enough to remember the Savings & Loans crisis. The herding behavior of thousands of small institutions may be very destructive. I am trying to model investors’ behavior by proposing a precise definition of sentiment that is econometrically testable, linked to the pricing kernel. Also I am interested in the herding generated by regulation itself. Rules meant to increase safety may force institutions toward holding similar portfolios and also to attempt making simultaneous adjustments. This behavior increases the likelihood of a crash. What do you see as academically exciting? I enjoy financial research because it brings together ideas from a wide variety of academic disciplines. Some of the most innovative theories I am excited about are based on the analysis of the stability of complex systems, the neurological basis of investment decisions, and the legal foundations of financial systems. On a more applied level, I am interested in the design of a new financial architecture to replace the economic functions of the financial system displaced by the new safety regulations. Whenever we say that banks should reduce their exposure to a market, we leave unanswered q","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"2 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69722858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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