题目：Linking Agent-Based Models and Stochastic Models of Financial Markets
时间：6月18日(星期一) 下午2:30-4:30
地点：上海理工大学 管理学院第二会议室（至四楼出电梯后去往左边的附楼）。
联系人: ---
主讲人：Feng Ling
Biography: Mr Feng Ling received his B.Sci (2008) from National University of Singapore, majoring in Physics with 2nd Upper Honors. In 2009, he received full scholarship for his PhD study supervised by Prof. Li Baowen at National University of Singapore, in the department of NUS Graduate School of Integrative Sciences and Engineering. In 2011, he joint Prof. H. Eugene Stanley’s group at Center for Polymer Studies in Boston University as a research scholar. His research interest is in econophysics, a growing discipline of physics that studies economics systems using the methods from physics. In particular, his focuses are on agent-based modeling, stochastic modeling, economic and financial networks.
报告介绍:
"Linking agent-based models and stochastic models of financial markets", with Baowen Li, Boris Podobnik, Tobias Preis, and H. Eugene Stanley, published in "Proceedings of National Academy of Sciences, May 29, 2012 vol. 109 no. 22 8388-8393".
It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that “fat”tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.
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